Import Libraries

import pandas as pd
import numpy as np

import cv2
import os

import matplotlib.pyplot as plt
from matplotlib import rcParams
%matplotlib inline

import seaborn as sns

from zipfile import ZipFile

# Ignore the warnings
import warnings
warnings.filterwarnings("ignore")

from keras import Model
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, Flatten, Dense, BatchNormalization, Dropout, GlobalAveragePooling2D
from keras.callbacks import ReduceLROnPlateau
from keras.optimizers import Adam
import multiprocessing

from keras.applications import MobileNetV2, mobilenet_v2, ResNet50, resnet, vgg19

from sklearn.metrics import classification_report

import shutil
import csv

import glob
from IPython.display import Image, display

import torch

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

1. Import the data.

with ZipFile('/content/drive/MyDrive/AIML_CAPSTONE/Car+Images.zip', 'r') as z:
  z.extractall()

with ZipFile('/content/drive/MyDrive/AIML_CAPSTONE/Annotations.zip', 'r') as z:
  z.extractall()

Read Annotations Data

anno_train = pd.read_csv("Annotations/Train Annotations.csv")
anno_test = pd.read_csv("Annotations/Test Annotation.csv")

anno_train.head()

	Image Name	Bounding Box coordinates	Unnamed: 2	Unnamed: 3	Unnamed: 4	Image class
0	00001.jpg	39	116	569	375	14
1	00002.jpg	36	116	868	587	3
2	00003.jpg	85	109	601	381	91
3	00004.jpg	621	393	1484	1096	134
4	00005.jpg	14	36	133	99	106

anno_test.head()

	Image Name	Bounding Box coordinates	Unnamed: 2	Unnamed: 3	Unnamed: 4	Image class
0	00001.jpg	30	52	246	147	181
1	00002.jpg	100	19	576	203	103
2	00003.jpg	51	105	968	659	145
3	00004.jpg	67	84	581	407	187
4	00005.jpg	140	151	593	339	185

print("The shape of anno_train: ", anno_train.shape)
print("The shape of anno_test: ", anno_test.shape)

The shape of anno_train:  (8144, 6)
The shape of anno_test:  (8041, 6)

col_names = ['image_name', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] # renaming columns

anno_train.columns = col_names
anno_test.columns = col_names

anno_train.head()

	image_name	xmin	ymin	xmax	ymax	class
0	00001.jpg	39	116	569	375	14
1	00002.jpg	36	116	868	587	3
2	00003.jpg	85	109	601	381	91
3	00004.jpg	621	393	1484	1096	134
4	00005.jpg	14	36	133	99	106

anno_test.head()

	image_name	xmin	ymin	xmax	ymax	class
0	00001.jpg	30	52	246	147	181
1	00002.jpg	100	19	576	203	103
2	00003.jpg	51	105	968	659	145
3	00004.jpg	67	84	581	407	187
4	00005.jpg	140	151	593	339	185

Read car names data and create dictionary with corresponding class number

car_names = pd.read_csv("/content/drive/MyDrive/AIML_CAPSTONE/Car+names+and+make.csv", header=None)
car_names.loc[173][0] = "Ram C-V Cargo Van Minivan 2012"
car_names.head()

	0
0	AM General Hummer SUV 2000
1	Acura RL Sedan 2012
2	Acura TL Sedan 2012
3	Acura TL Type-S 2008
4	Acura TSX Sedan 2012

# Create a dictionary to fetch car name easily from the class label
keys = list(range(1,197))
values = list(car_names[0])

car_names_dict = dict(zip(keys, values))
car_names_dict[1]

'AM General Hummer SUV 2000'

TRAIN_FOLDER_PATH = 'Car Images/Train Images'
TEST_FOLDER_PATH = 'Car Images/Test Images'

2. Map training and testing images to its classes.

3. Map training and testing images to its annotations.

• Note that loading a large number of images into a dataframe or a numpy.array is not advisable. When we try to load all the images, RAM is overflowing. Hence, I gave a threshold 'max_images' to only load a limited number of images.
• For model training, we'll be using ImageDataGenerator to feed all the images to model during runtime. This is the ideal way rather than feeding it from dataframe.
• To access any image easily, we'll store image_path in train_metadata and test_metadata dataframes instead of storing the entire image.
• Doing both of the above questions in a single step.

def load_images(path, max_images):

    images = []
    image_names = []

    for folder_name in os.listdir(path): # Iterate over each folder present in 'train' folder

        if not folder_name.startswith('.'): # Ignore hidden folders

            for file_name in os.listdir(os.path.join(path, folder_name)): # Iterate over each file

                img_path = os.path.join(path, folder_name, file_name) # Form image path

                img = cv2.imread(img_path)

                image_names.append(file_name)
                images.append(img)

                if(len(images) >= max_images): # Added this condition to load only limited number of images. To not overuse RAM.
                  return image_names, images    

Mapping train images to their class and annotations.

train_image_names, train_images = load_images(path = TRAIN_FOLDER_PATH, max_images = 50) # load only 50 images

train_image_data = pd.DataFrame({
    'image_name': train_image_names, 
    'image': train_images})

train_image_data = train_image_data.merge(anno_train, on='image_name') # Map images to class and annotations

train_image_data['car_name'] = train_image_data['class'].map(car_names_dict)

train_image_data.head()

	image_name	image	xmin	ymin	xmax	ymax	class	car_name
0	02642.jpg	[[[154, 153, 155], [147, 146, 148], [150, 149,...	9	12	595	354	185	Tesla Model S Sedan 2012
1	02261.jpg	[[[202, 161, 139], [198, 159, 137], [201, 161,...	76	31	574	261	185	Tesla Model S Sedan 2012
2	06367.jpg	[[[2, 8, 15], [2, 8, 15], [2, 8, 13], [1, 7, 1...	10	80	576	317	185	Tesla Model S Sedan 2012
3	03864.jpg	[[[33, 24, 27], [19, 21, 22], [12, 20, 20], [2...	40	101	549	360	185	Tesla Model S Sedan 2012
4	03873.jpg	[[[206, 226, 244], [206, 226, 244], [207, 227,...	15	111	489	258	185	Tesla Model S Sedan 2012

Mapping test images to their class and annotations.

test_image_names, test_images = load_images(path = TEST_FOLDER_PATH, max_images = 50) # load only 50 images

test_image_data = pd.DataFrame({
    'image_name': test_image_names, 
    'image': test_images})

test_image_data = test_image_data.merge(anno_test, on='image_name') # Map images to class and annotations

test_image_data['car_name'] = test_image_data['class'].map(car_names_dict)

test_image_data.head()

	image_name	image	xmin	ymin	xmax	ymax	class	car_name
0	05224.jpg	[[[190, 235, 255], [192, 235, 255], [192, 234,...	28	65	572	374	185	Tesla Model S Sedan 2012
1	04106.jpg	[[[218, 193, 177], [218, 193, 177], [218, 193,...	21	141	573	321	185	Tesla Model S Sedan 2012
2	02488.jpg	[[[233, 226, 207], [229, 222, 203], [227, 220,...	43	60	595	322	185	Tesla Model S Sedan 2012
3	05800.jpg	[[[243, 241, 241], [243, 241, 241], [243, 241,...	21	206	605	422	185	Tesla Model S Sedan 2012
4	04710.jpg	[[[70, 88, 125], [80, 98, 135], [89, 107, 144]...	75	214	594	494	185	Tesla Model S Sedan 2012

Load image metadata

def load_image_metadata(path):

    image_paths = []
    image_names = []
    image_widths = []
    image_heights = []
    image_channels = []

    for folder_name in os.listdir(path): # Iterate over each folder present in 'train' folder

        if not folder_name.startswith('.'): # Ignore hidden folders

            for file_name in os.listdir(os.path.join(path, folder_name)): # Iterate over each file

                img_path = os.path.join(path, folder_name, file_name) # Form image path

                img = cv2.imread(img_path)

                image_paths.append(img_path)
                image_names.append(file_name)
                image_heights.append(img.shape[0])
                image_widths.append(img.shape[1])
                image_channels.append(img.shape[2])

    return image_paths, image_names, image_heights, image_widths, image_channels

def load_metadata(path, annotations):
  
  image_metadata = load_image_metadata(path)
  metadata = pd.DataFrame({
      'image_path': image_metadata[0],
      'image_name': image_metadata[1], 
      'image_height': image_metadata[2], 
      'image_width': image_metadata[3], 
      'image_channels': image_metadata[4]})
  
  metadata = metadata.merge(annotations, on='image_name')
  
  metadata['car_name'] = metadata['class'].map(car_names_dict)
  
  return metadata

train_metadata = load_metadata(TRAIN_FOLDER_PATH, anno_train)
train_metadata.head()

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name
0	Car Images/Train Images/Tesla Model S Sedan 20...	02642.jpg	356	609	3	9	12	595	354	185	Tesla Model S Sedan 2012
1	Car Images/Train Images/Tesla Model S Sedan 20...	02261.jpg	342	600	3	76	31	574	261	185	Tesla Model S Sedan 2012
2	Car Images/Train Images/Tesla Model S Sedan 20...	06367.jpg	352	576	3	10	80	576	317	185	Tesla Model S Sedan 2012
3	Car Images/Train Images/Tesla Model S Sedan 20...	03864.jpg	385	580	3	40	101	549	360	185	Tesla Model S Sedan 2012
4	Car Images/Train Images/Tesla Model S Sedan 20...	03873.jpg	333	500	3	15	111	489	258	185	Tesla Model S Sedan 2012

test_metadata = load_metadata(TEST_FOLDER_PATH, anno_test)
test_metadata.head()

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name
0	Car Images/Test Images/Tesla Model S Sedan 201...	05224.jpg	390	590	3	28	65	572	374	185	Tesla Model S Sedan 2012
1	Car Images/Test Images/Tesla Model S Sedan 201...	04106.jpg	371	580	3	21	141	573	321	185	Tesla Model S Sedan 2012
2	Car Images/Test Images/Tesla Model S Sedan 201...	02488.jpg	334	630	3	43	60	595	322	185	Tesla Model S Sedan 2012
3	Car Images/Test Images/Tesla Model S Sedan 201...	05800.jpg	480	640	3	21	206	605	422	185	Tesla Model S Sedan 2012
4	Car Images/Test Images/Tesla Model S Sedan 201...	04710.jpg	495	640	3	75	214	594	494	185	Tesla Model S Sedan 2012

train_metadata.shape

(8144, 11)

test_metadata.shape

(8041, 11)

def display_image(image_name, is_train=True):
  
  folder_path = TRAIN_FOLDER_PATH if is_train else TEST_FOLDER_PATH

  if(is_train):
    matching_row = train_metadata[train_metadata['image_name'] == image_name]
  else:
    matching_row = test_metadata[test_metadata['image_name'] == image_name]

  car_name = matching_row['car_name'].iloc[0]

  image_path = os.path.join(folder_path, car_name, image_name)

  
  img = cv2.imread(image_path)

  print('Name: ', car_name)
  print('Shape: ', img.shape)
  plt.imshow(img);
  plt.show()
  print()

for i in list(train_metadata.sample(5).image_name): # display 5 sample images
  display_image(i)

Name:  Audi S5 Coupe 2012
Shape:  (420, 560, 3)

Name:  Ford F-450 Super Duty Crew Cab 2012
Shape:  (768, 1024, 3)

Name:  Dodge Caliber Wagon 2012
Shape:  (1080, 1920, 3)

Name:  Lincoln Town Car Sedan 2011
Shape:  (242, 300, 3)

Name:  GMC Yukon Hybrid SUV 2012
Shape:  (480, 640, 3)

4. Display images with bounding box.

def draw_bounding_box(image, bounding_box):

  img_rec = cv2.rectangle(image, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), (255,0,0), 2)
  
  plt.imshow(img_rec);
    
  plt.show()

def display_image_with_bounding_box(image_name, is_train=True):
  
  folder_path = TRAIN_FOLDER_PATH if is_train else TEST_FOLDER_PATH

  if(is_train):
    matching_row = train_metadata[train_metadata['image_name'] == image_name]
  else:
    matching_row = test_metadata[test_metadata['image_name'] == image_name]

  car_name = matching_row['car_name'].iloc[0]

  image_path = os.path.join(folder_path, car_name, image_name)
  bounding_box = [matching_row['xmin'].iloc[0], matching_row['ymin'].iloc[0], matching_row['xmax'].iloc[0], matching_row['ymax'].iloc[0]]

  img = cv2.imread(image_path)

  print('Name: ', car_name)
  print('Shape: ', img.shape)
  print('Bounding Box: ', bounding_box)
  
  draw_bounding_box(img, bounding_box)
  print()

for i in list(train_metadata.sample(5).image_name): # display 5 sample images with bounding box
  display_image_with_bounding_box(i)

Name:  Ford F-150 Regular Cab 2007
Shape:  (360, 480, 3)
Bounding Box:  [4, 42, 416, 345]

Name:  Acura TSX Sedan 2012
Shape:  (200, 300, 3)
Bounding Box:  [19, 49, 283, 191]

Name:  Bentley Continental GT Coupe 2012
Shape:  (213, 300, 3)
Bounding Box:  [12, 105, 282, 209]

Name:  Honda Accord Coupe 2012
Shape:  (194, 259, 3)
Bounding Box:  [20, 54, 244, 154]

Name:  Dodge Challenger SRT8 2011
Shape:  (333, 500, 3)
Bounding Box:  [14, 77, 489, 287]

EDA

Check if any missing data

train_metadata.isnull().sum()

image_path        0
image_name        0
image_height      0
image_width       0
image_channels    0
xmin              0
ymin              0
xmax              0
ymax              0
class             0
car_name          0
dtype: int64

test_metadata.isnull().sum()

image_path        0
image_name        0
image_height      0
image_width       0
image_channels    0
xmin              0
ymin              0
xmax              0
ymax              0
class             0
car_name          0
dtype: int64

train_metadata.isna().sum()

image_path        0
image_name        0
image_height      0
image_width       0
image_channels    0
xmin              0
ymin              0
xmax              0
ymax              0
class             0
car_name          0
dtype: int64

train_metadata.isna().sum()

image_path        0
image_name        0
image_height      0
image_width       0
image_channels    0
xmin              0
ymin              0
xmax              0
ymax              0
class             0
car_name          0
dtype: int64

• There are no missing values in both train and test dataset.

Analyse 5-number summary

train_metadata.describe().transpose()

	count	mean	std	min	25%	50%	75%	max
image_height	8144.0	482.771979	317.580021	57.0	290.0	424.0	540.00	3744.0
image_width	8144.0	699.793099	450.922445	78.0	429.0	636.5	800.00	5616.0
image_channels	8144.0	3.000000	0.000000	3.0	3.0	3.0	3.00	3.0
xmin	8144.0	64.906803	82.198684	1.0	19.0	39.0	79.00	1648.0
ymin	8144.0	108.661223	104.551635	1.0	42.0	80.0	138.25	1508.0
xmax	8144.0	638.208620	410.776734	76.0	392.0	569.0	746.25	5205.0
ymax	8144.0	416.431606	273.786000	47.0	248.0	360.0	477.00	3389.0
class	8144.0	98.979371	56.503148	1.0	50.0	99.0	148.00	196.0

test_metadata.describe().transpose()

	count	mean	std	min	25%	50%	75%	max
image_height	8041.0	483.749658	319.083857	41.0	289.0	426.0	549.0	5400.0
image_width	8041.0	701.177092	455.601639	78.0	432.0	640.0	800.0	7800.0
image_channels	8041.0	3.000000	0.000000	3.0	3.0	3.0	3.0	3.0
xmin	8041.0	65.057456	82.336438	1.0	19.0	39.0	78.0	1048.0
ymin	8041.0	107.816192	108.052354	1.0	40.0	79.0	137.0	1651.0
xmax	8041.0	639.433777	411.632197	72.0	395.0	575.0	748.0	7224.0
ymax	8041.0	417.301828	274.659479	41.0	249.0	364.0	478.0	3835.0
class	8041.0	98.975501	56.505578	1.0	51.0	99.0	148.0	196.0

Common observations on both train and test data:

• The count is same for all the fields indicating no missing values.
• The image_height, image_width, xmin, ymin, xmax, ymax have different means and std deviations indicating that all the images may not have the same shape.
• Min and max values of class are 1 and 196 respectively indicating 196 total number of classes.
• For 'image_channels', both min and max are 3 and has no null values. All the train and test images have 3 channels only. Since it's constant, we can safely remove this column from both train_metadata and test_metadata.

train_metadata.drop('image_channels', axis=1, inplace=True)
test_metadata.drop('image_channels', axis=1, inplace=True)

Analysis on class distribution in both train and test sets.

print('The number of unique cars in train set : ', train_metadata['car_name'].nunique())
print('The number of unique cars in test set : ', test_metadata['car_name'].nunique())

The number of unique cars in train set :  196
The number of unique cars in test set :  196

train_metadata['car_name'].value_counts()

GMC Savana Van 2012                                    68
Chrysler 300 SRT-8 2010                                49
Mercedes-Benz 300-Class Convertible 1993               48
Mitsubishi Lancer Sedan 2012                           48
Jaguar XK XKR 2012                                     47
                                                       ..
Rolls-Royce Phantom Drophead Coupe Convertible 2012    31
Chevrolet Express Cargo Van 2007                       30
Maybach Landaulet Convertible 2012                     29
FIAT 500 Abarth 2012                                   28
Hyundai Accent Sedan 2012                              24
Name: car_name, Length: 196, dtype: int64

test_metadata['car_name'].value_counts()

GMC Savana Van 2012                                    68
Chrysler 300 SRT-8 2010                                48
Mercedes-Benz 300-Class Convertible 1993               48
Mitsubishi Lancer Sedan 2012                           47
Audi S6 Sedan 2011                                     46
                                                       ..
Rolls-Royce Phantom Drophead Coupe Convertible 2012    30
Maybach Landaulet Convertible 2012                     29
Chevrolet Express Cargo Van 2007                       29
FIAT 500 Abarth 2012                                   27
Hyundai Accent Sedan 2012                              24
Name: car_name, Length: 196, dtype: int64

list(train_metadata['car_name'].unique()) # print all unique values

['Rolls-Royce Phantom Sedan 2012',
 'Acura Integra Type R 2001',
 'Hyundai Sonata Sedan 2012',
 'Chevrolet Malibu Hybrid Sedan 2010',
 'Ford Mustang Convertible 2007',
 'Dodge Ram Pickup 3500 Crew Cab 2010',
 'Mitsubishi Lancer Sedan 2012',
 'GMC Terrain SUV 2012',
 'Chevrolet Tahoe Hybrid SUV 2012',
 'Dodge Dakota Club Cab 2007',
 'Suzuki SX4 Sedan 2012',
 'Hyundai Accent Sedan 2012',
 'Acura TL Sedan 2012',
 'Honda Odyssey Minivan 2007',
 'Bentley Continental Supersports Conv. Convertible 2012',
 'Suzuki Aerio Sedan 2007',
 'Audi A5 Coupe 2012',
 'Dodge Dakota Crew Cab 2010',
 'Dodge Charger SRT-8 2009',
 'Audi S4 Sedan 2012',
 'Bentley Continental Flying Spur Sedan 2007',
 'Hyundai Veracruz SUV 2012',
 'Chevrolet Express Cargo Van 2007',
 'Hyundai Santa Fe SUV 2012',
 'Audi S5 Coupe 2012',
 'McLaren MP4-12C Coupe 2012',
 'Dodge Charger Sedan 2012',
 'Rolls-Royce Phantom Drophead Coupe Convertible 2012',
 'Chevrolet Silverado 1500 Extended Cab 2012',
 'Ford Fiesta Sedan 2012',
 'Nissan Leaf Hatchback 2012',
 'Jaguar XK XKR 2012',
 'Cadillac Escalade EXT Crew Cab 2007',
 'Audi V8 Sedan 1994',
 'Chevrolet Express Van 2007',
 'Toyota 4Runner SUV 2012',
 'Hyundai Elantra Touring Hatchback 2012',
 'Ford Expedition EL SUV 2009',
 'Chevrolet Corvette Ron Fellows Edition Z06 2007',
 'BMW X5 SUV 2007',
 'Chrysler 300 SRT-8 2010',
 'Ford F-150 Regular Cab 2012',
 'Mercedes-Benz C-Class Sedan 2012',
 'Dodge Sprinter Cargo Van 2009',
 'FIAT 500 Convertible 2012',
 'Dodge Challenger SRT8 2011',
 'Ford Focus Sedan 2007',
 'Land Rover Range Rover SUV 2012',
 'Plymouth Neon Coupe 1999',
 'Chevrolet Malibu Sedan 2007',
 'Honda Odyssey Minivan 2012',
 'Hyundai Veloster Hatchback 2012',
 'Toyota Camry Sedan 2012',
 'Ford F-150 Regular Cab 2007',
 'Ferrari FF Coupe 2012',
 'Dodge Journey SUV 2012',
 'BMW 3 Series Sedan 2012',
 'BMW 6 Series Convertible 2007',
 'Volkswagen Golf Hatchback 1991',
 'Suzuki SX4 Hatchback 2012',
 'Hyundai Genesis Sedan 2012',
 'Jeep Patriot SUV 2012',
 'Chrysler Crossfire Convertible 2008',
 'Chevrolet Avalanche Crew Cab 2012',
 'Acura RL Sedan 2012',
 'Buick Enclave SUV 2012',
 'Maybach Landaulet Convertible 2012',
 'Dodge Caliber Wagon 2012',
 'Chevrolet HHR SS 2010',
 'GMC Acadia SUV 2012',
 'Hyundai Tucson SUV 2012',
 'Chrysler Aspen SUV 2009',
 'Dodge Magnum Wagon 2008',
 'Geo Metro Convertible 1993',
 'Toyota Corolla Sedan 2012',
 'Bentley Mulsanne Sedan 2011',
 'Dodge Durango SUV 2012',
 'Honda Accord Coupe 2012',
 'Chevrolet Silverado 1500 Classic Extended Cab 2007',
 'Audi TT RS Coupe 2012',
 'Chevrolet Monte Carlo Coupe 2007',
 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012',
 'Volkswagen Golf Hatchback 2012',
 'Mercedes-Benz SL-Class Coupe 2009',
 'Ford F-450 Super Duty Crew Cab 2012',
 'Audi TT Hatchback 2011',
 'Chevrolet Corvette Convertible 2012',
 'smart fortwo Convertible 2012',
 'Aston Martin V8 Vantage Coupe 2012',
 'BMW 1 Series Convertible 2012',
 'BMW Z4 Convertible 2012',
 'Ford Ranger SuperCab 2011',
 'Acura TL Type-S 2008',
 'Jeep Compass SUV 2012',
 'Spyker C8 Convertible 2009',
 'BMW ActiveHybrid 5 Sedan 2012',
 'Lamborghini Reventon Coupe 2008',
 'Nissan 240SX Coupe 1998',
 'Bentley Arnage Sedan 2009',
 'BMW M5 Sedan 2010',
 'Nissan NV Passenger Van 2012',
 'Volvo XC90 SUV 2007',
 'HUMMER H2 SUT Crew Cab 2009',
 'BMW 3 Series Wagon 2012',
 'Mercedes-Benz Sprinter Van 2012',
 'Eagle Talon Hatchback 1998',
 'Infiniti G Coupe IPL 2012',
 'Isuzu Ascender SUV 2008',
 'Chevrolet Cobalt SS 2010',
 'BMW M6 Convertible 2010',
 'BMW M3 Coupe 2012',
 'GMC Canyon Extended Cab 2012',
 'Chrysler Sebring Convertible 2010',
 'Suzuki Kizashi Sedan 2012',
 'Chevrolet TrailBlazer SS 2009',
 'Chevrolet Camaro Convertible 2012',
 'Audi S5 Convertible 2012',
 'Volkswagen Beetle Hatchback 2012',
 'BMW X3 SUV 2012',
 'Chevrolet Impala Sedan 2007',
 'Fisker Karma Sedan 2012',
 'BMW X6 SUV 2012',
 'Hyundai Azera Sedan 2012',
 'Audi 100 Sedan 1994',
 'Volvo C30 Hatchback 2012',
 'Ford Freestar Minivan 2007',
 'Jeep Grand Cherokee SUV 2012',
 'Honda Accord Sedan 2012',
 'Bentley Continental GT Coupe 2007',
 'AM General Hummer SUV 2000',
 'Mercedes-Benz 300-Class Convertible 1993',
 'Toyota Sequoia SUV 2012',
 'Lamborghini Aventador Coupe 2012',
 'Lamborghini Gallardo LP 570-4 Superleggera 2012',
 'Dodge Ram Pickup 3500 Quad Cab 2009',
 'Daewoo Nubira Wagon 2002',
 'Acura TSX Sedan 2012',
 'Audi S6 Sedan 2011',
 'Audi S4 Sedan 2007',
 'Chevrolet Silverado 2500HD Regular Cab 2012',
 'Dodge Caliber Wagon 2007',
 'Audi R8 Coupe 2012',
 'FIAT 500 Abarth 2012',
 'Land Rover LR2 SUV 2012',
 'Bugatti Veyron 16.4 Convertible 2009',
 'Mazda Tribute SUV 2011',
 'Rolls-Royce Ghost Sedan 2012',
 'Bugatti Veyron 16.4 Coupe 2009',
 'Ram C/V Cargo Van Minivan 2012',
 'Hyundai Elantra Sedan 2007',
 'Dodge Caravan Minivan 1997',
 'Scion xD Hatchback 2012',
 'Chevrolet Corvette ZR1 2012',
 'Aston Martin Virage Convertible 2012',
 'Chevrolet Silverado 1500 Regular Cab 2012',
 'Jeep Liberty SUV 2012',
 'Chevrolet Sonic Sedan 2012',
 'Chevrolet Traverse SUV 2012',
 'Buick Regal GS 2012',
 'Cadillac SRX SUV 2012',
 'Ferrari 458 Italia Coupe 2012',
 'Mercedes-Benz S-Class Sedan 2012',
 'Porsche Panamera Sedan 2012',
 'Audi 100 Wagon 1994',
 'MINI Cooper Roadster Convertible 2012',
 'Hyundai Sonata Hybrid Sedan 2012',
 'Cadillac CTS-V Sedan 2012',
 'Ferrari 458 Italia Convertible 2012',
 'Volvo 240 Sedan 1993',
 'Aston Martin V8 Vantage Convertible 2012',
 'Buick Rainier SUV 2007',
 'GMC Savana Van 2012',
 'Spyker C8 Coupe 2009',
 'Jeep Wrangler SUV 2012',
 'HUMMER H3T Crew Cab 2010',
 'Infiniti QX56 SUV 2011',
 'Buick Verano Sedan 2012',
 'Ford GT Coupe 2006',
 'Lamborghini Diablo Coupe 2001',
 'Ford Edge SUV 2012',
 'Bentley Continental GT Coupe 2012',
 'Chrysler PT Cruiser Convertible 2008',
 'Ferrari California Convertible 2012',
 'BMW 1 Series Coupe 2012',
 'Acura ZDX Hatchback 2012',
 'Tesla Model S Sedan 2012',
 'Lincoln Town Car Sedan 2011',
 'Audi TTS Coupe 2012',
 'Ford E-Series Wagon Van 2012',
 'Chrysler Town and Country Minivan 2012',
 'Dodge Durango SUV 2007',
 'Aston Martin Virage Coupe 2012',
 'Audi RS 4 Convertible 2008',
 'Nissan Juke Hatchback 2012',
 'Mercedes-Benz E-Class Sedan 2012',
 'GMC Yukon Hybrid SUV 2012']

Class distribution in train data

rcParams['figure.figsize'] = 70,10

x = train_metadata['car_name'].value_counts()
labels = list(x.index)
values = list(x)

g = sns.barplot(x = labels, y = values)
g.set_xticklabels(labels=labels, rotation=90)

plt.xlabel('Car Name')
plt.ylabel('Count')
plt.title('Train Data Frequency')
plt.show()

Class distribution in test data

rcParams['figure.figsize'] = 70,10

x = test_metadata['car_name'].value_counts()
labels = list(x.index)
values = list(x)

g = sns.barplot(x = labels, y = values)
g.set_xticklabels(labels=labels, rotation=90)

plt.xlabel('Car Name')
plt.ylabel('Count')
plt.title('Test Data Frequency')
plt.show()

Observations

• The total number of images per class are slightly varying within the train and test sets, but for a given class, the number of images in train and test sets is similar.
• For eg, The highest number of images are present for the car ‘GMC Savana Van 2012’ in both train and test sets and the lowest number of images are present for the car ‘Hyundai Accent Sedan 2012 in both train and test sets.
• This indicates that the class imabalance does not affect the model much and do not make the model unfairly biased.

Train Images Metadata Pairplot

g = sns.pairplot(train_metadata, diag_kind='kde')
g.fig.suptitle("Train Images Metadata Pairplot", y=1.02)
plt.show()

Test Images Metadata Pairplot

g = sns.pairplot(test_metadata, diag_kind='kde')
g.fig.suptitle("Test Images Metadata Pairplot", y=1.02)
plt.show()

rcParams['figure.figsize'] = 6.4,4.8 # set back the default figsize 

Common observations from train and test pairplots

• The distribution of image_height, image_width, xmin, ymin, xmax, ymax is right skewed. This indicates that, there is high frequency of small sized images i.e. the images with large height and width are very rare.
• The disribution of ‘class’ is highly uniform. This indicates that all classes of images are roughly equally distributed within the train and test datasets.
• There is positive correlation among (xmax, image_width), (ymax, image_height) and among various other fields because an image with large height and width tend to have a large bounding box. Since all the bounding boxes are supposed to be within the image, all the images should follow the condition xmin < xmax < image_width and similarly, ymin < ymax < image_height. Hence, the strong correlation.

Check if the bounding boxes are within the image boundaries for all the images.

• To verify this we must check if (xmin, ymin) and (xmax, ymax) are within the image width and height range.

train_metadata[(train_metadata['xmin'] > train_metadata['xmax']) | (train_metadata['xmax'] > train_metadata['image_width'])]

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name
5646	Car Images/Train Images/BMW X5 SUV 2007/07389.jpg	07389.jpg	768	576	3	56	89	717	519	32	BMW X5 SUV 2007

train_metadata[(train_metadata['ymin'] > train_metadata['ymax']) | (train_metadata['ymax'] > train_metadata['image_height'])]

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name

test_metadata[(test_metadata['xmin'] > test_metadata['xmax']) | (test_metadata['xmax'] > test_metadata['image_width'])]

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name

test_metadata[(test_metadata['ymin'] > test_metadata['ymax']) | (test_metadata['ymax'] > test_metadata['image_height'])]

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name

We found out one faulty bounding box from train set for the image '07389.jpg' as shown below.

display_image_with_bounding_box('07389.jpg')

Name:  BMW X5 SUV 2007
Shape:  (768, 576, 3)
Bounding Box:  [56, 89, 717, 519]

The image is rotated, hence the discrepency. We'll correct it by rotating it.

image_path = "Car Images/Train Images/BMW X5 SUV 2007/07389.jpg"
image = cv2.imread(image_path)

image_rot = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE) # rotate the image and save it
cv2.imwrite(image_path, image_rot)

True

display_image_with_bounding_box('07389.jpg') # display the image after adjustment

Name:  BMW X5 SUV 2007
Shape:  (576, 768, 3)
Bounding Box:  [56, 89, 717, 519]

# As we rotated the image, Flip the height and width in the metadata as well
row_index = train_metadata[train_metadata['image_name'] == '07389.jpg'].index[0]

original_width = train_metadata.at[row_index, 'image_width']
original_height = train_metadata.at[row_index, 'image_height']

# Flip
train_metadata.at[row_index, 'image_width'] = original_height
train_metadata.at[row_index, 'image_height'] = original_width

train_metadata[(train_metadata['xmin'] > train_metadata['xmax']) | (train_metadata['xmax'] > train_metadata['image_width'])] # verify that condition is satisfied after correction

	image_path	image_name	image_height	image_width	image_channels	xmin	ymin	xmax	ymax	class	car_name

5. Design, train and test basic CNN models to classify the car

MOBILENET V2

# Using augmentation on train set
train_datagen=ImageDataGenerator(rotation_range=15,
                                  width_shift_range=0.1,
                                  height_shift_range=0.1,
                                  zoom_range=0.2,
                                  horizontal_flip=True,
                                  preprocessing_function=mobilenet_v2.preprocess_input)

valid_datagen=ImageDataGenerator(horizontal_flip=True, 
                                  preprocessing_function=mobilenet_v2.preprocess_input)

train_generator=train_datagen.flow_from_directory(
    directory=TRAIN_FOLDER_PATH,
    batch_size=64,
    seed=42,
    target_size=(224,224))


valid_generator=valid_datagen.flow_from_directory(
    directory=TEST_FOLDER_PATH,
    batch_size=300,
    seed=42,
    target_size=(224,224))

Found 8144 images belonging to 196 classes.
Found 8041 images belonging to 196 classes.

for x,y in valid_generator:
    x_val = x
    y_val = y
    break;

base_model = MobileNetV2(input_shape=(224,224,3), alpha = 0.5, include_top=False, weights='imagenet')

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(.6)(x)
prediction_layer = Dense(196, activation='softmax')(x)

model=Model(inputs=base_model.input, outputs=prediction_layer)

# Freezing first 80 layers out of total 156 layers
for layer in model.layers[:80]:
    layer.trainable=False
for layer in model.layers[80:]:
    layer.trainable=True

model.compile(optimizer = Adam(lr=0.0001, clipnorm=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224_no_top.h5
3201480/3201480 [==============================] - 0s 0us/step

model.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 Conv1 (Conv2D)                 (None, 112, 112, 16  432         ['input_1[0][0]']                
                                )                                                                 
                                                                                                  
 bn_Conv1 (BatchNormalization)  (None, 112, 112, 16  64          ['Conv1[0][0]']                  
                                )                                                                 
                                                                                                  
 Conv1_relu (ReLU)              (None, 112, 112, 16  0           ['bn_Conv1[0][0]']               
                                )                                                                 
                                                                                                  
 expanded_conv_depthwise (Depth  (None, 112, 112, 16  144        ['Conv1_relu[0][0]']             
 wiseConv2D)                    )                                                                 
                                                                                                  
 expanded_conv_depthwise_BN (Ba  (None, 112, 112, 16  64         ['expanded_conv_depthwise[0][0]']
 tchNormalization)              )                                                                 
                                                                                                  
 expanded_conv_depthwise_relu (  (None, 112, 112, 16  0          ['expanded_conv_depthwise_BN[0][0
 ReLU)                          )                                ]']                              
                                                                                                  
 expanded_conv_project (Conv2D)  (None, 112, 112, 8)  128        ['expanded_conv_depthwise_relu[0]
                                                                 [0]']                            
                                                                                                  
 expanded_conv_project_BN (Batc  (None, 112, 112, 8)  32         ['expanded_conv_project[0][0]']  
 hNormalization)                                                                                  
                                                                                                  
 block_1_expand (Conv2D)        (None, 112, 112, 48  384         ['expanded_conv_project_BN[0][0]'
                                )                                ]                                
                                                                                                  
 block_1_expand_BN (BatchNormal  (None, 112, 112, 48  192        ['block_1_expand[0][0]']         
 ization)                       )                                                                 
                                                                                                  
 block_1_expand_relu (ReLU)     (None, 112, 112, 48  0           ['block_1_expand_BN[0][0]']      
                                )                                                                 
                                                                                                  
 block_1_pad (ZeroPadding2D)    (None, 113, 113, 48  0           ['block_1_expand_relu[0][0]']    
                                )                                                                 
                                                                                                  
 block_1_depthwise (DepthwiseCo  (None, 56, 56, 48)  432         ['block_1_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_1_depthwise_BN (BatchNor  (None, 56, 56, 48)  192         ['block_1_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_1_depthwise_relu (ReLU)  (None, 56, 56, 48)   0           ['block_1_depthwise_BN[0][0]']   
                                                                                                  
 block_1_project (Conv2D)       (None, 56, 56, 16)   768         ['block_1_depthwise_relu[0][0]'] 
                                                                                                  
 block_1_project_BN (BatchNorma  (None, 56, 56, 16)  64          ['block_1_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_2_expand (Conv2D)        (None, 56, 56, 96)   1536        ['block_1_project_BN[0][0]']     
                                                                                                  
 block_2_expand_BN (BatchNormal  (None, 56, 56, 96)  384         ['block_2_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_2_expand_relu (ReLU)     (None, 56, 56, 96)   0           ['block_2_expand_BN[0][0]']      
                                                                                                  
 block_2_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_2_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_2_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_2_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_2_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_2_depthwise_BN[0][0]']   
                                                                                                  
 block_2_project (Conv2D)       (None, 56, 56, 16)   1536        ['block_2_depthwise_relu[0][0]'] 
                                                                                                  
 block_2_project_BN (BatchNorma  (None, 56, 56, 16)  64          ['block_2_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_2_add (Add)              (None, 56, 56, 16)   0           ['block_1_project_BN[0][0]',     
                                                                  'block_2_project_BN[0][0]']     
                                                                                                  
 block_3_expand (Conv2D)        (None, 56, 56, 96)   1536        ['block_2_add[0][0]']            
                                                                                                  
 block_3_expand_BN (BatchNormal  (None, 56, 56, 96)  384         ['block_3_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_3_expand_relu (ReLU)     (None, 56, 56, 96)   0           ['block_3_expand_BN[0][0]']      
                                                                                                  
 block_3_pad (ZeroPadding2D)    (None, 57, 57, 96)   0           ['block_3_expand_relu[0][0]']    
                                                                                                  
 block_3_depthwise (DepthwiseCo  (None, 28, 28, 96)  864         ['block_3_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_3_depthwise_BN (BatchNor  (None, 28, 28, 96)  384         ['block_3_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_3_depthwise_relu (ReLU)  (None, 28, 28, 96)   0           ['block_3_depthwise_BN[0][0]']   
                                                                                                  
 block_3_project (Conv2D)       (None, 28, 28, 16)   1536        ['block_3_depthwise_relu[0][0]'] 
                                                                                                  
 block_3_project_BN (BatchNorma  (None, 28, 28, 16)  64          ['block_3_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_4_expand (Conv2D)        (None, 28, 28, 96)   1536        ['block_3_project_BN[0][0]']     
                                                                                                  
 block_4_expand_BN (BatchNormal  (None, 28, 28, 96)  384         ['block_4_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_4_expand_relu (ReLU)     (None, 28, 28, 96)   0           ['block_4_expand_BN[0][0]']      
                                                                                                  
 block_4_depthwise (DepthwiseCo  (None, 28, 28, 96)  864         ['block_4_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_4_depthwise_BN (BatchNor  (None, 28, 28, 96)  384         ['block_4_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_4_depthwise_relu (ReLU)  (None, 28, 28, 96)   0           ['block_4_depthwise_BN[0][0]']   
                                                                                                  
 block_4_project (Conv2D)       (None, 28, 28, 16)   1536        ['block_4_depthwise_relu[0][0]'] 
                                                                                                  
 block_4_project_BN (BatchNorma  (None, 28, 28, 16)  64          ['block_4_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_4_add (Add)              (None, 28, 28, 16)   0           ['block_3_project_BN[0][0]',     
                                                                  'block_4_project_BN[0][0]']     
                                                                                                  
 block_5_expand (Conv2D)        (None, 28, 28, 96)   1536        ['block_4_add[0][0]']            
                                                                                                  
 block_5_expand_BN (BatchNormal  (None, 28, 28, 96)  384         ['block_5_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_5_expand_relu (ReLU)     (None, 28, 28, 96)   0           ['block_5_expand_BN[0][0]']      
                                                                                                  
 block_5_depthwise (DepthwiseCo  (None, 28, 28, 96)  864         ['block_5_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_5_depthwise_BN (BatchNor  (None, 28, 28, 96)  384         ['block_5_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_5_depthwise_relu (ReLU)  (None, 28, 28, 96)   0           ['block_5_depthwise_BN[0][0]']   
                                                                                                  
 block_5_project (Conv2D)       (None, 28, 28, 16)   1536        ['block_5_depthwise_relu[0][0]'] 
                                                                                                  
 block_5_project_BN (BatchNorma  (None, 28, 28, 16)  64          ['block_5_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_5_add (Add)              (None, 28, 28, 16)   0           ['block_4_add[0][0]',            
                                                                  'block_5_project_BN[0][0]']     
                                                                                                  
 block_6_expand (Conv2D)        (None, 28, 28, 96)   1536        ['block_5_add[0][0]']            
                                                                                                  
 block_6_expand_BN (BatchNormal  (None, 28, 28, 96)  384         ['block_6_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_6_expand_relu (ReLU)     (None, 28, 28, 96)   0           ['block_6_expand_BN[0][0]']      
                                                                                                  
 block_6_pad (ZeroPadding2D)    (None, 29, 29, 96)   0           ['block_6_expand_relu[0][0]']    
                                                                                                  
 block_6_depthwise (DepthwiseCo  (None, 14, 14, 96)  864         ['block_6_pad[0][0]']            
 nv2D)                                                                                            
                                                                                                  
 block_6_depthwise_BN (BatchNor  (None, 14, 14, 96)  384         ['block_6_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_6_depthwise_relu (ReLU)  (None, 14, 14, 96)   0           ['block_6_depthwise_BN[0][0]']   
                                                                                                  
 block_6_project (Conv2D)       (None, 14, 14, 32)   3072        ['block_6_depthwise_relu[0][0]'] 
                                                                                                  
 block_6_project_BN (BatchNorma  (None, 14, 14, 32)  128         ['block_6_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_7_expand (Conv2D)        (None, 14, 14, 192)  6144        ['block_6_project_BN[0][0]']     
                                                                                                  
 block_7_expand_BN (BatchNormal  (None, 14, 14, 192)  768        ['block_7_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_7_expand_relu (ReLU)     (None, 14, 14, 192)  0           ['block_7_expand_BN[0][0]']      
                                                                                                  
 block_7_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_7_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_7_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_7_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_7_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_7_depthwise_BN[0][0]']   
                                                                                                  
 block_7_project (Conv2D)       (None, 14, 14, 32)   6144        ['block_7_depthwise_relu[0][0]'] 
                                                                                                  
 block_7_project_BN (BatchNorma  (None, 14, 14, 32)  128         ['block_7_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_7_add (Add)              (None, 14, 14, 32)   0           ['block_6_project_BN[0][0]',     
                                                                  'block_7_project_BN[0][0]']     
                                                                                                  
 block_8_expand (Conv2D)        (None, 14, 14, 192)  6144        ['block_7_add[0][0]']            
                                                                                                  
 block_8_expand_BN (BatchNormal  (None, 14, 14, 192)  768        ['block_8_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_8_expand_relu (ReLU)     (None, 14, 14, 192)  0           ['block_8_expand_BN[0][0]']      
                                                                                                  
 block_8_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_8_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_8_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_8_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_8_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_8_depthwise_BN[0][0]']   
                                                                                                  
 block_8_project (Conv2D)       (None, 14, 14, 32)   6144        ['block_8_depthwise_relu[0][0]'] 
                                                                                                  
 block_8_project_BN (BatchNorma  (None, 14, 14, 32)  128         ['block_8_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_8_add (Add)              (None, 14, 14, 32)   0           ['block_7_add[0][0]',            
                                                                  'block_8_project_BN[0][0]']     
                                                                                                  
 block_9_expand (Conv2D)        (None, 14, 14, 192)  6144        ['block_8_add[0][0]']            
                                                                                                  
 block_9_expand_BN (BatchNormal  (None, 14, 14, 192)  768        ['block_9_expand[0][0]']         
 ization)                                                                                         
                                                                                                  
 block_9_expand_relu (ReLU)     (None, 14, 14, 192)  0           ['block_9_expand_BN[0][0]']      
                                                                                                  
 block_9_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_9_expand_relu[0][0]']    
 nv2D)                                                                                            
                                                                                                  
 block_9_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_9_depthwise[0][0]']      
 malization)                                                                                      
                                                                                                  
 block_9_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_9_depthwise_BN[0][0]']   
                                                                                                  
 block_9_project (Conv2D)       (None, 14, 14, 32)   6144        ['block_9_depthwise_relu[0][0]'] 
                                                                                                  
 block_9_project_BN (BatchNorma  (None, 14, 14, 32)  128         ['block_9_project[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_9_add (Add)              (None, 14, 14, 32)   0           ['block_8_add[0][0]',            
                                                                  'block_9_project_BN[0][0]']     
                                                                                                  
 block_10_expand (Conv2D)       (None, 14, 14, 192)  6144        ['block_9_add[0][0]']            
                                                                                                  
 block_10_expand_BN (BatchNorma  (None, 14, 14, 192)  768        ['block_10_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_10_expand_relu (ReLU)    (None, 14, 14, 192)  0           ['block_10_expand_BN[0][0]']     
                                                                                                  
 block_10_depthwise (DepthwiseC  (None, 14, 14, 192)  1728       ['block_10_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_10_depthwise_BN (BatchNo  (None, 14, 14, 192)  768        ['block_10_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_10_depthwise_relu (ReLU)  (None, 14, 14, 192)  0          ['block_10_depthwise_BN[0][0]']  
                                                                                                  
 block_10_project (Conv2D)      (None, 14, 14, 48)   9216        ['block_10_depthwise_relu[0][0]']
                                                                                                  
 block_10_project_BN (BatchNorm  (None, 14, 14, 48)  192         ['block_10_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_11_expand (Conv2D)       (None, 14, 14, 288)  13824       ['block_10_project_BN[0][0]']    
                                                                                                  
 block_11_expand_BN (BatchNorma  (None, 14, 14, 288)  1152       ['block_11_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_11_expand_relu (ReLU)    (None, 14, 14, 288)  0           ['block_11_expand_BN[0][0]']     
                                                                                                  
 block_11_depthwise (DepthwiseC  (None, 14, 14, 288)  2592       ['block_11_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_11_depthwise_BN (BatchNo  (None, 14, 14, 288)  1152       ['block_11_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_11_depthwise_relu (ReLU)  (None, 14, 14, 288)  0          ['block_11_depthwise_BN[0][0]']  
                                                                                                  
 block_11_project (Conv2D)      (None, 14, 14, 48)   13824       ['block_11_depthwise_relu[0][0]']
                                                                                                  
 block_11_project_BN (BatchNorm  (None, 14, 14, 48)  192         ['block_11_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_11_add (Add)             (None, 14, 14, 48)   0           ['block_10_project_BN[0][0]',    
                                                                  'block_11_project_BN[0][0]']    
                                                                                                  
 block_12_expand (Conv2D)       (None, 14, 14, 288)  13824       ['block_11_add[0][0]']           
                                                                                                  
 block_12_expand_BN (BatchNorma  (None, 14, 14, 288)  1152       ['block_12_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_12_expand_relu (ReLU)    (None, 14, 14, 288)  0           ['block_12_expand_BN[0][0]']     
                                                                                                  
 block_12_depthwise (DepthwiseC  (None, 14, 14, 288)  2592       ['block_12_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_12_depthwise_BN (BatchNo  (None, 14, 14, 288)  1152       ['block_12_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_12_depthwise_relu (ReLU)  (None, 14, 14, 288)  0          ['block_12_depthwise_BN[0][0]']  
                                                                                                  
 block_12_project (Conv2D)      (None, 14, 14, 48)   13824       ['block_12_depthwise_relu[0][0]']
                                                                                                  
 block_12_project_BN (BatchNorm  (None, 14, 14, 48)  192         ['block_12_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_12_add (Add)             (None, 14, 14, 48)   0           ['block_11_add[0][0]',           
                                                                  'block_12_project_BN[0][0]']    
                                                                                                  
 block_13_expand (Conv2D)       (None, 14, 14, 288)  13824       ['block_12_add[0][0]']           
                                                                                                  
 block_13_expand_BN (BatchNorma  (None, 14, 14, 288)  1152       ['block_13_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_13_expand_relu (ReLU)    (None, 14, 14, 288)  0           ['block_13_expand_BN[0][0]']     
                                                                                                  
 block_13_pad (ZeroPadding2D)   (None, 15, 15, 288)  0           ['block_13_expand_relu[0][0]']   
                                                                                                  
 block_13_depthwise (DepthwiseC  (None, 7, 7, 288)   2592        ['block_13_pad[0][0]']           
 onv2D)                                                                                           
                                                                                                  
 block_13_depthwise_BN (BatchNo  (None, 7, 7, 288)   1152        ['block_13_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_13_depthwise_relu (ReLU)  (None, 7, 7, 288)   0           ['block_13_depthwise_BN[0][0]']  
                                                                                                  
 block_13_project (Conv2D)      (None, 7, 7, 80)     23040       ['block_13_depthwise_relu[0][0]']
                                                                                                  
 block_13_project_BN (BatchNorm  (None, 7, 7, 80)    320         ['block_13_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_14_expand (Conv2D)       (None, 7, 7, 480)    38400       ['block_13_project_BN[0][0]']    
                                                                                                  
 block_14_expand_BN (BatchNorma  (None, 7, 7, 480)   1920        ['block_14_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_14_expand_relu (ReLU)    (None, 7, 7, 480)    0           ['block_14_expand_BN[0][0]']     
                                                                                                  
 block_14_depthwise (DepthwiseC  (None, 7, 7, 480)   4320        ['block_14_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_14_depthwise_BN (BatchNo  (None, 7, 7, 480)   1920        ['block_14_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_14_depthwise_relu (ReLU)  (None, 7, 7, 480)   0           ['block_14_depthwise_BN[0][0]']  
                                                                                                  
 block_14_project (Conv2D)      (None, 7, 7, 80)     38400       ['block_14_depthwise_relu[0][0]']
                                                                                                  
 block_14_project_BN (BatchNorm  (None, 7, 7, 80)    320         ['block_14_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_14_add (Add)             (None, 7, 7, 80)     0           ['block_13_project_BN[0][0]',    
                                                                  'block_14_project_BN[0][0]']    
                                                                                                  
 block_15_expand (Conv2D)       (None, 7, 7, 480)    38400       ['block_14_add[0][0]']           
                                                                                                  
 block_15_expand_BN (BatchNorma  (None, 7, 7, 480)   1920        ['block_15_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_15_expand_relu (ReLU)    (None, 7, 7, 480)    0           ['block_15_expand_BN[0][0]']     
                                                                                                  
 block_15_depthwise (DepthwiseC  (None, 7, 7, 480)   4320        ['block_15_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_15_depthwise_BN (BatchNo  (None, 7, 7, 480)   1920        ['block_15_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_15_depthwise_relu (ReLU)  (None, 7, 7, 480)   0           ['block_15_depthwise_BN[0][0]']  
                                                                                                  
 block_15_project (Conv2D)      (None, 7, 7, 80)     38400       ['block_15_depthwise_relu[0][0]']
                                                                                                  
 block_15_project_BN (BatchNorm  (None, 7, 7, 80)    320         ['block_15_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 block_15_add (Add)             (None, 7, 7, 80)     0           ['block_14_add[0][0]',           
                                                                  'block_15_project_BN[0][0]']    
                                                                                                  
 block_16_expand (Conv2D)       (None, 7, 7, 480)    38400       ['block_15_add[0][0]']           
                                                                                                  
 block_16_expand_BN (BatchNorma  (None, 7, 7, 480)   1920        ['block_16_expand[0][0]']        
 lization)                                                                                        
                                                                                                  
 block_16_expand_relu (ReLU)    (None, 7, 7, 480)    0           ['block_16_expand_BN[0][0]']     
                                                                                                  
 block_16_depthwise (DepthwiseC  (None, 7, 7, 480)   4320        ['block_16_expand_relu[0][0]']   
 onv2D)                                                                                           
                                                                                                  
 block_16_depthwise_BN (BatchNo  (None, 7, 7, 480)   1920        ['block_16_depthwise[0][0]']     
 rmalization)                                                                                     
                                                                                                  
 block_16_depthwise_relu (ReLU)  (None, 7, 7, 480)   0           ['block_16_depthwise_BN[0][0]']  
                                                                                                  
 block_16_project (Conv2D)      (None, 7, 7, 160)    76800       ['block_16_depthwise_relu[0][0]']
                                                                                                  
 block_16_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_16_project[0][0]']       
 alization)                                                                                       
                                                                                                  
 Conv_1 (Conv2D)                (None, 7, 7, 1280)   204800      ['block_16_project_BN[0][0]']    
                                                                                                  
 Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 
                                                                                                  
 out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              
                                                                                                  
 global_average_pooling2d (Glob  (None, 1280)        0           ['out_relu[0][0]']               
 alAveragePooling2D)                                                                              
                                                                                                  
 dropout (Dropout)              (None, 1280)         0           ['global_average_pooling2d[0][0]'
                                                                 ]                                
                                                                                                  
 dense (Dense)                  (None, 196)          251076      ['dropout[0][0]']                
                                                                                                  
==================================================================================================
Total params: 957,300
Trainable params: 883,140
Non-trainable params: 74,160
__________________________________________________________________________________________________

history = model.fit(train_generator,
          steps_per_epoch=100,
          validation_data=(x_val,y_val),
          epochs=40,verbose=1,
          workers=multiprocessing.cpu_count())

Epoch 1/40
100/100 [==============================] - 100s 823ms/step - loss: 5.7576 - accuracy: 0.0074 - val_loss: 5.3402 - val_accuracy: 0.0100
Epoch 2/40
100/100 [==============================] - 83s 825ms/step - loss: 5.3191 - accuracy: 0.0142 - val_loss: 5.1847 - val_accuracy: 0.0200
Epoch 3/40
100/100 [==============================] - 84s 836ms/step - loss: 5.0863 - accuracy: 0.0282 - val_loss: 4.9885 - val_accuracy: 0.0367
Epoch 4/40
100/100 [==============================] - 85s 840ms/step - loss: 4.8492 - accuracy: 0.0506 - val_loss: 4.6850 - val_accuracy: 0.0533
Epoch 5/40
100/100 [==============================] - 84s 831ms/step - loss: 4.5621 - accuracy: 0.0814 - val_loss: 4.3946 - val_accuracy: 0.0900
Epoch 6/40
100/100 [==============================] - 86s 840ms/step - loss: 4.2925 - accuracy: 0.1047 - val_loss: 4.1448 - val_accuracy: 0.1233
Epoch 7/40
100/100 [==============================] - 84s 825ms/step - loss: 4.0267 - accuracy: 0.1321 - val_loss: 3.8453 - val_accuracy: 0.1633
Epoch 8/40
100/100 [==============================] - 84s 831ms/step - loss: 3.7582 - accuracy: 0.1708 - val_loss: 3.5926 - val_accuracy: 0.2200
Epoch 9/40
100/100 [==============================] - 83s 824ms/step - loss: 3.4942 - accuracy: 0.2077 - val_loss: 3.3117 - val_accuracy: 0.2400
Epoch 10/40
100/100 [==============================] - 82s 817ms/step - loss: 3.2652 - accuracy: 0.2443 - val_loss: 3.1124 - val_accuracy: 0.3000
Epoch 11/40
100/100 [==============================] - 86s 841ms/step - loss: 3.0364 - accuracy: 0.2933 - val_loss: 2.8669 - val_accuracy: 0.3333
Epoch 12/40
100/100 [==============================] - 84s 829ms/step - loss: 2.8460 - accuracy: 0.3232 - val_loss: 2.7549 - val_accuracy: 0.3500
Epoch 13/40
100/100 [==============================] - 87s 844ms/step - loss: 2.6662 - accuracy: 0.3512 - val_loss: 2.7059 - val_accuracy: 0.3567
Epoch 14/40
100/100 [==============================] - 85s 829ms/step - loss: 2.5133 - accuracy: 0.3807 - val_loss: 2.6008 - val_accuracy: 0.4033
Epoch 15/40
100/100 [==============================] - 85s 838ms/step - loss: 2.3706 - accuracy: 0.4064 - val_loss: 2.3489 - val_accuracy: 0.4533
Epoch 16/40
100/100 [==============================] - 84s 826ms/step - loss: 2.2189 - accuracy: 0.4389 - val_loss: 2.2038 - val_accuracy: 0.4967
Epoch 17/40
100/100 [==============================] - 85s 833ms/step - loss: 2.0920 - accuracy: 0.4636 - val_loss: 2.1767 - val_accuracy: 0.4733
Epoch 18/40
100/100 [==============================] - 84s 823ms/step - loss: 2.0144 - accuracy: 0.4838 - val_loss: 2.1247 - val_accuracy: 0.5000
Epoch 19/40
100/100 [==============================] - 84s 831ms/step - loss: 1.8796 - accuracy: 0.5166 - val_loss: 2.0612 - val_accuracy: 0.4833
Epoch 20/40
100/100 [==============================] - 86s 844ms/step - loss: 1.7845 - accuracy: 0.5376 - val_loss: 1.9446 - val_accuracy: 0.5233
Epoch 21/40
100/100 [==============================] - 84s 834ms/step - loss: 1.6948 - accuracy: 0.5568 - val_loss: 1.9627 - val_accuracy: 0.5200
Epoch 22/40
100/100 [==============================] - 85s 842ms/step - loss: 1.6083 - accuracy: 0.5735 - val_loss: 1.8468 - val_accuracy: 0.5500
Epoch 23/40
100/100 [==============================] - 85s 832ms/step - loss: 1.4947 - accuracy: 0.6053 - val_loss: 1.7701 - val_accuracy: 0.5800
Epoch 24/40
100/100 [==============================] - 86s 842ms/step - loss: 1.4754 - accuracy: 0.6048 - val_loss: 1.8193 - val_accuracy: 0.5433
Epoch 25/40
100/100 [==============================] - 88s 872ms/step - loss: 1.3841 - accuracy: 0.6239 - val_loss: 1.7330 - val_accuracy: 0.5533
Epoch 26/40
100/100 [==============================] - 85s 842ms/step - loss: 1.3139 - accuracy: 0.6433 - val_loss: 1.6806 - val_accuracy: 0.5633
Epoch 27/40
100/100 [==============================] - 84s 830ms/step - loss: 1.2339 - accuracy: 0.6678 - val_loss: 1.6284 - val_accuracy: 0.5933
Epoch 28/40
100/100 [==============================] - 85s 845ms/step - loss: 1.2243 - accuracy: 0.6680 - val_loss: 1.5336 - val_accuracy: 0.6067
Epoch 29/40
100/100 [==============================] - 84s 834ms/step - loss: 1.1407 - accuracy: 0.6933 - val_loss: 1.5109 - val_accuracy: 0.6133
Epoch 30/40
100/100 [==============================] - 89s 866ms/step - loss: 1.0826 - accuracy: 0.7103 - val_loss: 1.4671 - val_accuracy: 0.6233
Epoch 31/40
100/100 [==============================] - 85s 845ms/step - loss: 1.0269 - accuracy: 0.7165 - val_loss: 1.4418 - val_accuracy: 0.6433
Epoch 32/40
100/100 [==============================] - 85s 832ms/step - loss: 0.9914 - accuracy: 0.7292 - val_loss: 1.4211 - val_accuracy: 0.6500
Epoch 33/40
100/100 [==============================] - 83s 824ms/step - loss: 0.9407 - accuracy: 0.7524 - val_loss: 1.3688 - val_accuracy: 0.6500
Epoch 34/40
100/100 [==============================] - 86s 839ms/step - loss: 0.9281 - accuracy: 0.7443 - val_loss: 1.3532 - val_accuracy: 0.6567
Epoch 35/40
100/100 [==============================] - 84s 829ms/step - loss: 0.8733 - accuracy: 0.7590 - val_loss: 1.3627 - val_accuracy: 0.6467
Epoch 36/40
100/100 [==============================] - 89s 862ms/step - loss: 0.8304 - accuracy: 0.7672 - val_loss: 1.2842 - val_accuracy: 0.6700
Epoch 37/40
100/100 [==============================] - 85s 846ms/step - loss: 0.8164 - accuracy: 0.7752 - val_loss: 1.2247 - val_accuracy: 0.6933
Epoch 38/40
100/100 [==============================] - 86s 836ms/step - loss: 0.7945 - accuracy: 0.7794 - val_loss: 1.1975 - val_accuracy: 0.6933
Epoch 39/40
100/100 [==============================] - 84s 833ms/step - loss: 0.7491 - accuracy: 0.7895 - val_loss: 1.2045 - val_accuracy: 0.6800
Epoch 40/40
100/100 [==============================] - 88s 859ms/step - loss: 0.7061 - accuracy: 0.8073 - val_loss: 1.1337 - val_accuracy: 0.6867

def plot_loss(history):

  loss = history.history['loss']
  val_loss = history.history['val_loss']
  epochs = range(1,len(loss)+1)

  plt.plot(epochs, loss, label='Training Loss')
  plt.plot(epochs, val_loss, label='Validation Loss')

  plt.xlabel('Epochs')
  plt.ylabel('Loss')
  plt.legend()
  plt.show()

def plot_accuracy(history):
  
  accuracy = history.history['accuracy']
  val_accuracy = history.history['val_accuracy']
  epochs = range(1,len(accuracy)+1)
  
  plt.plot(epochs, accuracy, label='Training Accuracy')
  plt.plot(epochs, val_accuracy, label='Validation Accuracy')
  
  plt.xlabel('Epochs')
  plt.ylabel('Accuracy')
  plt.legend()
  
  plt.show()

plot_accuracy(history)

plot_loss(history)

for i,j in valid_generator:
    pred = model.predict(i)
    pred = pred.argmax(-1)
    true = j.argmax(-1)
    print(classification_report(true, pred))
    break;

10/10 [==============================] - 0s 30ms/step
              precision    recall  f1-score   support

           1       1.00      1.00      1.00         1
           2       0.50      0.50      0.50         2
           3       0.00      0.00      0.00         0
           4       0.00      0.00      0.00         1
           5       1.00      1.00      1.00         1
           6       1.00      0.50      0.67         2
           7       0.00      0.00      0.00         1
           8       1.00      0.50      0.67         2
           9       0.50      1.00      0.67         1
          11       0.00      0.00      0.00         2
          12       0.33      0.50      0.40         2
          13       1.00      1.00      1.00         2
          14       0.00      0.00      0.00         1
          15       0.00      0.00      0.00         0
          16       0.50      1.00      0.67         1
          17       1.00      0.33      0.50         3
          18       1.00      1.00      1.00         3
          19       0.75      0.75      0.75         4
          20       1.00      0.60      0.75         5
          21       0.67      1.00      0.80         2
          22       1.00      1.00      1.00         1
          23       1.00      1.00      1.00         1
          24       1.00      0.33      0.50         3
          25       1.00      1.00      1.00         1
          26       1.00      1.00      1.00         1
          27       0.00      0.00      0.00         1
          28       1.00      0.75      0.86         4
          29       1.00      0.67      0.80         3
          30       1.00      1.00      1.00         1
          32       0.67      1.00      0.80         2
          33       0.67      0.67      0.67         3
          35       1.00      1.00      1.00         1
          36       1.00      0.33      0.50         3
          37       1.00      0.50      0.67         2
          38       1.00      0.50      0.67         2
          39       0.50      1.00      0.67         1
          42       1.00      1.00      1.00         1
          43       0.50      1.00      0.67         1
          44       0.00      0.00      0.00         1
          45       0.00      0.00      0.00         0
          46       1.00      1.00      1.00         3
          47       1.00      1.00      1.00         1
          48       0.00      0.00      0.00         1
          49       0.00      0.00      0.00         0
          50       1.00      1.00      1.00         3
          51       1.00      0.33      0.50         3
          52       1.00      1.00      1.00         1
          53       0.50      0.33      0.40         3
          54       0.00      0.00      0.00         0
          55       1.00      1.00      1.00         1
          56       1.00      1.00      1.00         1
          57       1.00      0.50      0.67         4
          58       0.67      0.67      0.67         3
          59       1.00      0.67      0.80         3
          60       1.00      1.00      1.00         2
          62       0.33      0.50      0.40         2
          63       0.50      1.00      0.67         1
          64       0.00      0.00      0.00         0
          65       1.00      0.50      0.67         4
          66       1.00      1.00      1.00         1
          68       1.00      0.67      0.80         3
          69       0.50      1.00      0.67         2
          70       1.00      0.67      0.80         3
          71       1.00      0.50      0.67         2
          72       0.00      0.00      0.00         0
          73       1.00      1.00      1.00         2
          74       0.00      0.00      0.00         1
          75       0.00      0.00      0.00         1
          76       0.67      1.00      0.80         2
          77       1.00      1.00      1.00         2
          78       1.00      1.00      1.00         1
          79       1.00      1.00      1.00         1
          80       1.00      1.00      1.00         3
          81       0.67      0.67      0.67         3
          82       0.67      1.00      0.80         2
          83       1.00      0.33      0.50         3
          85       0.00      0.00      0.00         0
          86       0.00      0.00      0.00         1
          87       1.00      1.00      1.00         1
          88       1.00      0.50      0.67         2
          89       0.80      1.00      0.89         4
          91       0.67      1.00      0.80         2
          92       1.00      1.00      1.00         1
          93       1.00      1.00      1.00         2
          94       1.00      0.75      0.86         4
          95       1.00      1.00      1.00         3
          96       1.00      0.50      0.67         4
          97       1.00      1.00      1.00         1
          98       1.00      1.00      1.00         1
          99       1.00      1.00      1.00         1
         101       1.00      1.00      1.00         1
         102       1.00      1.00      1.00         2
         103       1.00      1.00      1.00         1
         104       1.00      1.00      1.00         4
         105       1.00      0.50      0.67         2
         106       1.00      0.50      0.67         2
         107       1.00      1.00      1.00         1
         108       1.00      0.50      0.67         2
         109       1.00      1.00      1.00         1
         111       1.00      1.00      1.00         1
         112       1.00      1.00      1.00         1
         113       0.00      0.00      0.00         1
         114       0.33      1.00      0.50         1
         115       1.00      0.50      0.67         4
         116       0.75      1.00      0.86         3
         117       0.50      0.33      0.40         3
         118       1.00      0.50      0.67         2
         119       0.67      1.00      0.80         2
         120       0.00      0.00      0.00         1
         121       0.00      0.00      0.00         1
         122       1.00      1.00      1.00         1
         124       1.00      1.00      1.00         1
         125       1.00      0.50      0.67         2
         126       0.00      0.00      0.00         0
         127       0.50      1.00      0.67         2
         128       0.80      1.00      0.89         4
         129       1.00      0.50      0.67         2
         130       1.00      1.00      1.00         3
         131       0.33      1.00      0.50         1
         132       1.00      1.00      1.00         1
         133       0.75      1.00      0.86         3
         134       1.00      1.00      1.00         2
         135       0.00      0.00      0.00         1
         136       1.00      1.00      1.00         2
         137       1.00      1.00      1.00         1
         139       0.00      0.00      0.00         0
         140       0.00      0.00      0.00         0
         142       1.00      0.67      0.80         3
         144       0.75      1.00      0.86         3
         146       1.00      0.50      0.67         2
         147       0.67      0.67      0.67         3
         152       0.00      0.00      0.00         0
         153       1.00      0.33      0.50         3
         154       0.00      0.00      0.00         1
         155       1.00      1.00      1.00         1
         156       1.00      1.00      1.00         1
         157       0.00      0.00      0.00         1
         158       0.00      0.00      0.00         0
         159       0.00      0.00      0.00         1
         160       1.00      1.00      1.00         1
         161       1.00      0.50      0.67         2
         162       1.00      0.50      0.67         2
         163       1.00      0.50      0.67         2
         164       0.60      1.00      0.75         3
         165       0.33      1.00      0.50         1
         166       0.00      0.00      0.00         1
         167       1.00      0.50      0.67         2
         169       1.00      1.00      1.00         3
         170       0.00      0.00      0.00         0
         171       0.00      0.00      0.00         0
         172       0.00      0.00      0.00         1
         173       1.00      0.50      0.67         2
         174       0.00      0.00      0.00         1
         176       1.00      1.00      1.00         2
         177       1.00      0.67      0.80         3
         178       0.33      1.00      0.50         2
         179       1.00      0.50      0.67         4
         180       0.25      0.50      0.33         2
         181       1.00      0.75      0.86         4
         182       1.00      1.00      1.00         1
         183       0.50      1.00      0.67         1
         185       1.00      1.00      1.00         1
         186       0.00      0.00      0.00         0
         187       1.00      0.33      0.50         3
         188       1.00      1.00      1.00         3
         189       1.00      0.67      0.80         3
         190       0.00      0.00      0.00         0
         191       1.00      0.50      0.67         2
         192       0.60      1.00      0.75         3

    accuracy                           0.70       300
   macro avg       0.69      0.63      0.63       300
weighted avg       0.82      0.70      0.72       300

RESNET50

train_datagen=ImageDataGenerator(rotation_range=15,
                                  width_shift_range=0.1,
                                  height_shift_range=0.1,
                                  zoom_range=0.2,
                                  horizontal_flip=True,
                                  preprocessing_function=resnet.preprocess_input)

valid_datagen=ImageDataGenerator(horizontal_flip=True, 
                                  preprocessing_function=resnet.preprocess_input)

train_generator=train_datagen.flow_from_directory(
    directory=TRAIN_FOLDER_PATH,
    batch_size=64,
    seed=42,
    target_size=(224,224))


valid_generator=valid_datagen.flow_from_directory(
    directory=TEST_FOLDER_PATH,
    batch_size=300,
    seed=42,
    target_size=(224,224))

Found 8144 images belonging to 196 classes.
Found 8041 images belonging to 196 classes.

for x,y in valid_generator:
    x_val = x
    y_val = y
    break;

base_model = ResNet50(input_shape=(224,224,3), include_top=False, weights='imagenet')

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.6)(x)
prediction_layer = Dense(196, activation='softmax')(x)

model=Model(inputs=base_model.input, outputs=prediction_layer)

# Freezing first 150 layers
for layer in model.layers[:150]:
    layer.trainable=False
for layer in model.layers[150:]:
    layer.trainable=True

model.compile(optimizer = Adam(lr=0.0001, clipnorm=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94765736/94765736 [==============================] - 0s 0us/step

model.summary()

Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_2[0][0]']                
                                                                                                  
 conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              
                                )                                                                 
                                                                                                  
 conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             
                                )                                                                 
                                                                                                  
 conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               
                                )                                                                 
                                                                                                  
 pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             
                                )                                                                 
                                                                                                  
 pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      
                                                                  'conv2_block1_3_bn[0][0]']      
                                                                                                  
 conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    
                                                                                                  
 conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       
                                                                  'conv2_block2_3_bn[0][0]']      
                                                                                                  
 conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    
                                                                                                  
 conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       
                                                                  'conv2_block3_3_bn[0][0]']      
                                                                                                  
 conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      
                                                                  'conv3_block1_3_bn[0][0]']      
                                                                                                  
 conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    
                                                                                                  
 conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       
                                                                  'conv3_block2_3_bn[0][0]']      
                                                                                                  
 conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    
                                                                                                  
 conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       
                                                                  'conv3_block3_3_bn[0][0]']      
                                                                                                  
 conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    
                                                                                                  
 conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       
                                                                  'conv3_block4_3_bn[0][0]']      
                                                                                                  
 conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      
                                )                                 'conv4_block1_3_bn[0][0]']      
                                                                                                  
 conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       
                                )                                 'conv4_block2_3_bn[0][0]']      
                                                                                                  
 conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       
                                )                                 'conv4_block3_3_bn[0][0]']      
                                                                                                  
 conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       
                                )                                 'conv4_block4_3_bn[0][0]']      
                                                                                                  
 conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       
                                )                                 'conv4_block5_3_bn[0][0]']      
                                                                                                  
 conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       
                                )                                 'conv4_block6_3_bn[0][0]']      
                                                                                                  
 conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      
                                                                  'conv5_block1_3_bn[0][0]']      
                                                                                                  
 conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    
                                                                                                  
 conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       
                                                                  'conv5_block2_3_bn[0][0]']      
                                                                                                  
 conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    
                                                                                                  
 conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       
                                                                  'conv5_block3_3_bn[0][0]']      
                                                                                                  
 conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       
                                                                                                  
 global_average_pooling2d_1 (Gl  (None, 2048)        0           ['conv5_block3_out[0][0]']       
 obalAveragePooling2D)                                                                            
                                                                                                  
 dropout_1 (Dropout)            (None, 2048)         0           ['global_average_pooling2d_1[0][0
                                                                 ]']                              
                                                                                                  
 dense_1 (Dense)                (None, 196)          401604      ['dropout_1[0][0]']              
                                                                                                  
==================================================================================================
Total params: 23,989,316
Trainable params: 10,391,748
Non-trainable params: 13,597,568
__________________________________________________________________________________________________

for i,layer in enumerate(model.layers): # Frozen 150 layers out of total 177 layers
  print(i, layer.name, layer.trainable) 

0 input_2 False
1 conv1_pad False
2 conv1_conv False
3 conv1_bn False
4 conv1_relu False
5 pool1_pad False
6 pool1_pool False
7 conv2_block1_1_conv False
8 conv2_block1_1_bn False
9 conv2_block1_1_relu False
10 conv2_block1_2_conv False
11 conv2_block1_2_bn False
12 conv2_block1_2_relu False
13 conv2_block1_0_conv False
14 conv2_block1_3_conv False
15 conv2_block1_0_bn False
16 conv2_block1_3_bn False
17 conv2_block1_add False
18 conv2_block1_out False
19 conv2_block2_1_conv False
20 conv2_block2_1_bn False
21 conv2_block2_1_relu False
22 conv2_block2_2_conv False
23 conv2_block2_2_bn False
24 conv2_block2_2_relu False
25 conv2_block2_3_conv False
26 conv2_block2_3_bn False
27 conv2_block2_add False
28 conv2_block2_out False
29 conv2_block3_1_conv False
30 conv2_block3_1_bn False
31 conv2_block3_1_relu False
32 conv2_block3_2_conv False
33 conv2_block3_2_bn False
34 conv2_block3_2_relu False
35 conv2_block3_3_conv False
36 conv2_block3_3_bn False
37 conv2_block3_add False
38 conv2_block3_out False
39 conv3_block1_1_conv False
40 conv3_block1_1_bn False
41 conv3_block1_1_relu False
42 conv3_block1_2_conv False
43 conv3_block1_2_bn False
44 conv3_block1_2_relu False
45 conv3_block1_0_conv False
46 conv3_block1_3_conv False
47 conv3_block1_0_bn False
48 conv3_block1_3_bn False
49 conv3_block1_add False
50 conv3_block1_out False
51 conv3_block2_1_conv False
52 conv3_block2_1_bn False
53 conv3_block2_1_relu False
54 conv3_block2_2_conv False
55 conv3_block2_2_bn False
56 conv3_block2_2_relu False
57 conv3_block2_3_conv False
58 conv3_block2_3_bn False
59 conv3_block2_add False
60 conv3_block2_out False
61 conv3_block3_1_conv False
62 conv3_block3_1_bn False
63 conv3_block3_1_relu False
64 conv3_block3_2_conv False
65 conv3_block3_2_bn False
66 conv3_block3_2_relu False
67 conv3_block3_3_conv False
68 conv3_block3_3_bn False
69 conv3_block3_add False
70 conv3_block3_out False
71 conv3_block4_1_conv False
72 conv3_block4_1_bn False
73 conv3_block4_1_relu False
74 conv3_block4_2_conv False
75 conv3_block4_2_bn False
76 conv3_block4_2_relu False
77 conv3_block4_3_conv False
78 conv3_block4_3_bn False
79 conv3_block4_add False
80 conv3_block4_out False
81 conv4_block1_1_conv False
82 conv4_block1_1_bn False
83 conv4_block1_1_relu False
84 conv4_block1_2_conv False
85 conv4_block1_2_bn False
86 conv4_block1_2_relu False
87 conv4_block1_0_conv False
88 conv4_block1_3_conv False
89 conv4_block1_0_bn False
90 conv4_block1_3_bn False
91 conv4_block1_add False
92 conv4_block1_out False
93 conv4_block2_1_conv False
94 conv4_block2_1_bn False
95 conv4_block2_1_relu False
96 conv4_block2_2_conv False
97 conv4_block2_2_bn False
98 conv4_block2_2_relu False
99 conv4_block2_3_conv False
100 conv4_block2_3_bn False
101 conv4_block2_add False
102 conv4_block2_out False
103 conv4_block3_1_conv False
104 conv4_block3_1_bn False
105 conv4_block3_1_relu False
106 conv4_block3_2_conv False
107 conv4_block3_2_bn False
108 conv4_block3_2_relu False
109 conv4_block3_3_conv False
110 conv4_block3_3_bn False
111 conv4_block3_add False
112 conv4_block3_out False
113 conv4_block4_1_conv False
114 conv4_block4_1_bn False
115 conv4_block4_1_relu False
116 conv4_block4_2_conv False
117 conv4_block4_2_bn False
118 conv4_block4_2_relu False
119 conv4_block4_3_conv False
120 conv4_block4_3_bn False
121 conv4_block4_add False
122 conv4_block4_out False
123 conv4_block5_1_conv False
124 conv4_block5_1_bn False
125 conv4_block5_1_relu False
126 conv4_block5_2_conv False
127 conv4_block5_2_bn False
128 conv4_block5_2_relu False
129 conv4_block5_3_conv False
130 conv4_block5_3_bn False
131 conv4_block5_add False
132 conv4_block5_out False
133 conv4_block6_1_conv False
134 conv4_block6_1_bn False
135 conv4_block6_1_relu False
136 conv4_block6_2_conv False
137 conv4_block6_2_bn False
138 conv4_block6_2_relu False
139 conv4_block6_3_conv False
140 conv4_block6_3_bn False
141 conv4_block6_add False
142 conv4_block6_out False
143 conv5_block1_1_conv False
144 conv5_block1_1_bn False
145 conv5_block1_1_relu False
146 conv5_block1_2_conv False
147 conv5_block1_2_bn False
148 conv5_block1_2_relu False
149 conv5_block1_0_conv False
150 conv5_block1_3_conv True
151 conv5_block1_0_bn True
152 conv5_block1_3_bn True
153 conv5_block1_add True
154 conv5_block1_out True
155 conv5_block2_1_conv True
156 conv5_block2_1_bn True
157 conv5_block2_1_relu True
158 conv5_block2_2_conv True
159 conv5_block2_2_bn True
160 conv5_block2_2_relu True
161 conv5_block2_3_conv True
162 conv5_block2_3_bn True
163 conv5_block2_add True
164 conv5_block2_out True
165 conv5_block3_1_conv True
166 conv5_block3_1_bn True
167 conv5_block3_1_relu True
168 conv5_block3_2_conv True
169 conv5_block3_2_bn True
170 conv5_block3_2_relu True
171 conv5_block3_3_conv True
172 conv5_block3_3_bn True
173 conv5_block3_add True
174 conv5_block3_out True
175 global_average_pooling2d_1 True
176 dropout_1 True
177 dense_1 True

history = model.fit(train_generator,
          steps_per_epoch=100,
          validation_data=(x_val,y_val),
          epochs=40,verbose=1,
          workers=multiprocessing.cpu_count())

Epoch 1/40
100/100 [==============================] - 96s 888ms/step - loss: 5.8212 - accuracy: 0.0083 - val_loss: 4.8994 - val_accuracy: 0.0333
Epoch 2/40
100/100 [==============================] - 88s 857ms/step - loss: 4.9720 - accuracy: 0.0414 - val_loss: 4.0736 - val_accuracy: 0.1567
Epoch 3/40
100/100 [==============================] - 105s 1s/step - loss: 4.2395 - accuracy: 0.1137 - val_loss: 3.2783 - val_accuracy: 0.2433
Epoch 4/40
100/100 [==============================] - 91s 886ms/step - loss: 3.5661 - accuracy: 0.2014 - val_loss: 2.8139 - val_accuracy: 0.3400
Epoch 5/40
100/100 [==============================] - 87s 854ms/step - loss: 2.9902 - accuracy: 0.2988 - val_loss: 2.5313 - val_accuracy: 0.4000
Epoch 6/40
100/100 [==============================] - 89s 874ms/step - loss: 2.5710 - accuracy: 0.3785 - val_loss: 2.3459 - val_accuracy: 0.4500
Epoch 7/40
100/100 [==============================] - 87s 858ms/step - loss: 2.1864 - accuracy: 0.4682 - val_loss: 1.9734 - val_accuracy: 0.5033
Epoch 8/40
100/100 [==============================] - 87s 859ms/step - loss: 1.8836 - accuracy: 0.5244 - val_loss: 1.8655 - val_accuracy: 0.5267
Epoch 9/40
100/100 [==============================] - 87s 849ms/step - loss: 1.6007 - accuracy: 0.5949 - val_loss: 1.7348 - val_accuracy: 0.5633
Epoch 10/40
100/100 [==============================] - 88s 863ms/step - loss: 1.3955 - accuracy: 0.6417 - val_loss: 1.6655 - val_accuracy: 0.5533
Epoch 11/40
100/100 [==============================] - 86s 847ms/step - loss: 1.1850 - accuracy: 0.6954 - val_loss: 1.5494 - val_accuracy: 0.6033
Epoch 12/40
100/100 [==============================] - 88s 869ms/step - loss: 1.0379 - accuracy: 0.7262 - val_loss: 1.4410 - val_accuracy: 0.6300
Epoch 13/40
100/100 [==============================] - 89s 876ms/step - loss: 0.9119 - accuracy: 0.7627 - val_loss: 1.4584 - val_accuracy: 0.6100
Epoch 14/40
100/100 [==============================] - 87s 846ms/step - loss: 0.7926 - accuracy: 0.7889 - val_loss: 1.4628 - val_accuracy: 0.5933
Epoch 15/40
100/100 [==============================] - 88s 859ms/step - loss: 0.6815 - accuracy: 0.8215 - val_loss: 1.4677 - val_accuracy: 0.5867
Epoch 16/40
100/100 [==============================] - 86s 837ms/step - loss: 0.6001 - accuracy: 0.8434 - val_loss: 1.3310 - val_accuracy: 0.6267
Epoch 17/40
100/100 [==============================] - 88s 861ms/step - loss: 0.5315 - accuracy: 0.8596 - val_loss: 1.4075 - val_accuracy: 0.6367
Epoch 18/40
100/100 [==============================] - 86s 843ms/step - loss: 0.4673 - accuracy: 0.8786 - val_loss: 1.3049 - val_accuracy: 0.6267
Epoch 19/40
100/100 [==============================] - 86s 847ms/step - loss: 0.3936 - accuracy: 0.8994 - val_loss: 1.3738 - val_accuracy: 0.6500
Epoch 20/40
100/100 [==============================] - 87s 874ms/step - loss: 0.3728 - accuracy: 0.9049 - val_loss: 1.3887 - val_accuracy: 0.6100
Epoch 21/40
100/100 [==============================] - 87s 864ms/step - loss: 0.3322 - accuracy: 0.9158 - val_loss: 1.3618 - val_accuracy: 0.6367
Epoch 22/40
100/100 [==============================] - 87s 857ms/step - loss: 0.2928 - accuracy: 0.9260 - val_loss: 1.3224 - val_accuracy: 0.6800
Epoch 23/40
100/100 [==============================] - 86s 848ms/step - loss: 0.2574 - accuracy: 0.9348 - val_loss: 1.3050 - val_accuracy: 0.6800
Epoch 24/40
100/100 [==============================] - 89s 870ms/step - loss: 0.2299 - accuracy: 0.9442 - val_loss: 1.3912 - val_accuracy: 0.6567
Epoch 25/40
100/100 [==============================] - 85s 838ms/step - loss: 0.2162 - accuracy: 0.9474 - val_loss: 1.4485 - val_accuracy: 0.6233
Epoch 26/40
100/100 [==============================] - 85s 843ms/step - loss: 0.2117 - accuracy: 0.9457 - val_loss: 1.4635 - val_accuracy: 0.6300
Epoch 27/40
100/100 [==============================] - 87s 856ms/step - loss: 0.1822 - accuracy: 0.9529 - val_loss: 1.5879 - val_accuracy: 0.6467
Epoch 28/40
100/100 [==============================] - 87s 855ms/step - loss: 0.1816 - accuracy: 0.9562 - val_loss: 1.3597 - val_accuracy: 0.6633
Epoch 29/40
100/100 [==============================] - 86s 849ms/step - loss: 0.1654 - accuracy: 0.9556 - val_loss: 1.4553 - val_accuracy: 0.6633
Epoch 30/40
100/100 [==============================] - 86s 853ms/step - loss: 0.1551 - accuracy: 0.9597 - val_loss: 1.3988 - val_accuracy: 0.6700
Epoch 31/40
100/100 [==============================] - 87s 863ms/step - loss: 0.1482 - accuracy: 0.9606 - val_loss: 1.3496 - val_accuracy: 0.6633
Epoch 32/40
100/100 [==============================] - 88s 865ms/step - loss: 0.1337 - accuracy: 0.9664 - val_loss: 1.4825 - val_accuracy: 0.6267
Epoch 33/40
100/100 [==============================] - 87s 847ms/step - loss: 0.1351 - accuracy: 0.9680 - val_loss: 1.5116 - val_accuracy: 0.6400
Epoch 34/40
100/100 [==============================] - 87s 858ms/step - loss: 0.1155 - accuracy: 0.9731 - val_loss: 1.4798 - val_accuracy: 0.6600
Epoch 35/40
100/100 [==============================] - 87s 847ms/step - loss: 0.1132 - accuracy: 0.9715 - val_loss: 1.4648 - val_accuracy: 0.6500
Epoch 36/40
100/100 [==============================] - 89s 868ms/step - loss: 0.1050 - accuracy: 0.9731 - val_loss: 1.4666 - val_accuracy: 0.6667
Epoch 37/40
100/100 [==============================] - 88s 871ms/step - loss: 0.1014 - accuracy: 0.9754 - val_loss: 1.4898 - val_accuracy: 0.6367
Epoch 38/40
100/100 [==============================] - 86s 852ms/step - loss: 0.0891 - accuracy: 0.9783 - val_loss: 1.5992 - val_accuracy: 0.6467
Epoch 39/40
100/100 [==============================] - 87s 854ms/step - loss: 0.0976 - accuracy: 0.9747 - val_loss: 1.4820 - val_accuracy: 0.6733
Epoch 40/40
100/100 [==============================] - 87s 861ms/step - loss: 0.0888 - accuracy: 0.9767 - val_loss: 1.5608 - val_accuracy: 0.6267

plot_loss(history)

plot_accuracy(history)

for i,j in valid_generator:
    pred = model.predict(i)
    pred = pred.argmax(-1)
    true = j.argmax(-1)
    print(classification_report(true, pred))
    break;

10/10 [==============================] - 2s 97ms/step
              precision    recall  f1-score   support

           0       0.50      1.00      0.67         1
           1       1.00      1.00      1.00         2
           3       1.00      1.00      1.00         2
           4       1.00      1.00      1.00         1
           5       0.50      1.00      0.67         1
           6       0.00      0.00      0.00         1
           7       0.00      0.00      0.00         1
           8       1.00      1.00      1.00         3
           9       1.00      1.00      1.00         1
          10       1.00      1.00      1.00         1
          11       1.00      1.00      1.00         1
          12       1.00      0.25      0.40         4
          13       0.75      1.00      0.86         3
          14       0.00      0.00      0.00         1
          15       0.00      0.00      0.00         1
          16       0.00      0.00      0.00         1
          17       0.00      0.00      0.00         2
          18       1.00      0.50      0.67         2
          19       0.00      0.00      0.00         2
          20       0.00      0.00      0.00         0
          21       0.50      1.00      0.67         2
          22       0.00      0.00      0.00         1
          24       1.00      1.00      1.00         2
          25       1.00      1.00      1.00         5
          26       1.00      1.00      1.00         2
          27       1.00      1.00      1.00         2
          28       1.00      0.75      0.86         4
          29       1.00      0.33      0.50         3
          30       1.00      1.00      1.00         1
          31       1.00      0.50      0.67         4
          32       0.50      0.50      0.50         4
          33       0.00      0.00      0.00         0
          35       0.50      1.00      0.67         1
          36       1.00      1.00      1.00         3
          37       0.00      0.00      0.00         1
          38       0.75      0.75      0.75         4
          39       0.50      1.00      0.67         1
          40       0.50      0.50      0.50         2
          41       0.00      0.00      0.00         1
          42       0.67      1.00      0.80         4
          43       0.50      1.00      0.67         1
          44       1.00      1.00      1.00         1
          45       1.00      0.50      0.67         2
          46       1.00      1.00      1.00         1
          47       1.00      0.50      0.67         2
          48       1.00      1.00      1.00         1
          49       0.00      0.00      0.00         0
          52       0.50      0.33      0.40         3
          53       0.00      0.00      0.00         1
          54       0.50      0.50      0.50         2
          55       1.00      0.33      0.50         3
          56       1.00      0.33      0.50         3
          57       1.00      1.00      1.00         2
          58       0.00      0.00      0.00         1
          59       1.00      1.00      1.00         1
          60       1.00      1.00      1.00         1
          61       1.00      0.75      0.86         4
          62       1.00      1.00      1.00         2
          63       0.50      0.50      0.50         2
          64       1.00      0.67      0.80         3
          65       0.00      0.00      0.00         1
          66       1.00      1.00      1.00         2
          69       1.00      1.00      1.00         1
          71       0.00      0.00      0.00         0
          72       0.50      0.50      0.50         2
          73       0.25      1.00      0.40         1
          74       1.00      0.33      0.50         3
          75       0.00      0.00      0.00         0
          76       0.50      0.50      0.50         2
          77       1.00      0.33      0.50         3
          78       1.00      1.00      1.00         1
          80       1.00      1.00      1.00         2
          81       1.00      1.00      1.00         1
          82       0.00      0.00      0.00         2
          83       0.20      0.50      0.29         2
          85       1.00      1.00      1.00         2
          86       0.00      0.00      0.00         0
          87       1.00      0.50      0.67         2
          88       0.00      0.00      0.00         1
          90       0.67      1.00      0.80         2
          91       1.00      1.00      1.00         1
          92       0.00      0.00      0.00         1
          93       0.50      1.00      0.67         1
          94       0.50      1.00      0.67         3
          95       1.00      0.50      0.67         2
          96       0.00      0.00      0.00         1
          97       1.00      1.00      1.00         1
          98       1.00      1.00      1.00         1
          99       1.00      1.00      1.00         1
         100       0.00      0.00      0.00         1
         102       0.50      1.00      0.67         2
         104       1.00      1.00      1.00         1
         107       1.00      1.00      1.00         5
         108       1.00      0.67      0.80         3
         109       1.00      1.00      1.00         1
         110       0.50      1.00      0.67         1
         111       0.40      1.00      0.57         2
         112       1.00      0.50      0.67         4
         113       1.00      1.00      1.00         1
         114       0.00      0.00      0.00         0
         117       0.33      1.00      0.50         1
         119       1.00      1.00      1.00         3
         120       1.00      1.00      1.00         1
         121       1.00      1.00      1.00         3
         122       1.00      1.00      1.00         2
         123       0.00      0.00      0.00         1
         124       0.00      0.00      0.00         1
         125       1.00      0.50      0.67         4
         127       1.00      0.50      0.67         4
         128       0.50      1.00      0.67         1
         129       0.00      0.00      0.00         3
         130       1.00      1.00      1.00         2
         131       0.00      0.00      0.00         1
         134       1.00      1.00      1.00         1
         136       1.00      1.00      1.00         1
         137       0.00      0.00      0.00         0
         139       0.33      0.50      0.40         2
         141       1.00      1.00      1.00         3
         142       1.00      0.67      0.80         3
         143       1.00      1.00      1.00         2
         144       1.00      0.33      0.50         3
         145       0.00      0.00      0.00         1
         146       0.00      0.00      0.00         1
         147       0.67      1.00      0.80         2
         148       0.00      0.00      0.00         1
         149       0.75      1.00      0.86         3
         150       1.00      1.00      1.00         1
         152       1.00      1.00      1.00         1
         153       0.00      0.00      0.00         1
         154       1.00      1.00      1.00         4
         155       1.00      0.50      0.67         2
         157       1.00      1.00      1.00         2
         158       0.00      0.00      0.00         0
         159       1.00      0.67      0.80         3
         160       1.00      0.67      0.80         3
         161       1.00      1.00      1.00         1
         162       0.33      1.00      0.50         1
         163       0.67      0.50      0.57         4
         164       0.00      0.00      0.00         1
         165       0.00      0.00      0.00         0
         166       0.50      1.00      0.67         1
         167       1.00      1.00      1.00         1
         168       0.00      0.00      0.00         0
         169       1.00      1.00      1.00         3
         172       1.00      0.67      0.80         3
         173       1.00      1.00      1.00         2
         174       1.00      0.50      0.67         2
         175       0.00      0.00      0.00         1
         176       0.50      1.00      0.67         1
         178       1.00      1.00      1.00         3
         179       0.00      0.00      0.00         0
         181       1.00      0.33      0.50         3
         182       0.14      1.00      0.25         1
         183       0.00      0.00      0.00         2
         184       0.50      1.00      0.67         2
         185       1.00      0.50      0.67         2
         186       0.75      1.00      0.86         3
         187       0.00      0.00      0.00         1
         188       1.00      1.00      1.00         1
         189       1.00      1.00      1.00         5
         190       0.67      1.00      0.80         4
         191       1.00      1.00      1.00         3
         193       0.00      0.00      0.00         0
         194       0.50      0.50      0.50         2
         195       1.00      0.33      0.50         3

    accuracy                           0.69       300
   macro avg       0.63      0.62      0.59       300
weighted avg       0.76      0.69      0.69       300

Comparing MobilenetV2 and Resnet50

• Resnet is aggressively learning and overfitting. We can see the validation_accuracy not growing while the training accuracy keeps increasing.We can reduce overfitting by doing more augmentation etc.
• Mobilenet showed smooth accuracy and loss curve for validation data.
• MobilenetV2 is clearly the winner as it has validation_accuracy of 68% while resenet being 62%. Mobilenet has better recall and F1 score.
• Please go through the interim report for deeper analysis on these two models.

Milestone 2

YoloV5

Install the necessary requirements for yolov5

!pip install openimages
!git clone https://github.com/ultralytics/yolov5
!pip install -U -r yolov5/requirements.txt

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting openimages
  Downloading openimages-0.0.1-py2.py3-none-any.whl (10 kB)
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Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-cpu>=2.1->cvdata->openimages) (4.9)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-cpu>=2.1->cvdata->openimages) (1.3.1)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow-cpu>=2.1->cvdata->openimages) (4.13.0)
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Collecting urllib3<1.27,>=1.25.4
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Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-cpu>=2.1->cvdata->openimages) (3.2.2)
Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from ImageHash->cvdata->openimages) (1.3.0)
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Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->openimages) (2022.6)
Installing collected packages: urllib3, jmespath, tensorflow-estimator, tensorboard, keras, flatbuffers, botocore, tensorflow-cpu, s3transfer, ImageHash, cvdata, boto3, openimages
  Attempting uninstall: urllib3
    Found existing installation: urllib3 1.24.3
    Uninstalling urllib3-1.24.3:
      Successfully uninstalled urllib3-1.24.3
  Attempting uninstall: tensorflow-estimator
    Found existing installation: tensorflow-estimator 2.9.0
    Uninstalling tensorflow-estimator-2.9.0:
      Successfully uninstalled tensorflow-estimator-2.9.0
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.9.1
    Uninstalling tensorboard-2.9.1:
      Successfully uninstalled tensorboard-2.9.1
  Attempting uninstall: keras
    Found existing installation: keras 2.9.0
    Uninstalling keras-2.9.0:
      Successfully uninstalled keras-2.9.0
  Attempting uninstall: flatbuffers
    Found existing installation: flatbuffers 1.12
    Uninstalling flatbuffers-1.12:
      Successfully uninstalled flatbuffers-1.12
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.9.2 requires flatbuffers<2,>=1.12, but you have flatbuffers 22.10.26 which is incompatible.
tensorflow 2.9.2 requires keras<2.10.0,>=2.9.0rc0, but you have keras 2.11.0 which is incompatible.
tensorflow 2.9.2 requires tensorboard<2.10,>=2.9, but you have tensorboard 2.11.0 which is incompatible.
tensorflow 2.9.2 requires tensorflow-estimator<2.10.0,>=2.9.0rc0, but you have tensorflow-estimator 2.11.0 which is incompatible.
Successfully installed ImageHash-4.3.1 boto3-1.26.13 botocore-1.29.13 cvdata-0.0.7 flatbuffers-22.10.26 jmespath-1.0.1 keras-2.11.0 openimages-0.0.1 s3transfer-0.6.0 tensorboard-2.11.0 tensorflow-cpu-2.11.0 tensorflow-estimator-2.11.0 urllib3-1.25.11

Cloning into 'yolov5'...
remote: Enumerating objects: 14027, done.
remote: Counting objects: 100% (261/261), done.
remote: Compressing objects: 100% (150/150), done.
remote: Total 14027 (delta 174), reused 166 (delta 111), pack-reused 13766
Receiving objects: 100% (14027/14027), 13.64 MiB | 17.83 MiB/s, done.
Resolving deltas: 100% (9601/9601), done.
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting gitpython
  Downloading GitPython-3.1.29-py3-none-any.whl (182 kB)
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Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from -r yolov5/requirements.txt (line 6)) (7.9.0)
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Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from -r yolov5/requirements.txt (line 7)) (3.2.2)
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.13.0 which is incompatible.
torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.13.0 which is incompatible.
google-colab 1.0.0 requires ipython~=7.9.0, but you have ipython 7.34.0 which is incompatible.
Successfully installed Pillow-9.3.0 fonttools-4.38.0 gitdb-4.0.9 gitpython-3.1.29 ipython-7.34.0 jedi-0.18.1 matplotlib-3.5.3 matplotlib-inline-0.1.6 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 psutil-5.9.4 requests-2.28.1 seaborn-0.12.1 smmap-5.0.0 thop-0.1.1.post2209072238 torch-1.13.0 torchvision-0.14.0

Pre-processing

Build a dictionary of image_name to bounding box coordinates

anno_dict = {}

filename = '/content/Annotations/Train Annotations.csv'
with open(filename, 'r') as csvfile:
    datareader = csv.reader(csvfile)
    for i,row in enumerate(datareader):
      if(i > 0):

        img_name = row[0]
        xmin = float(row[1])
        ymin = float(row[2])
        xmax = float(row[3])
        ymax = float(row[4])
        label = int(row[5]) - 1

        anno_dict[img_name] = (label, xmin, ymin, xmax, ymax)

filename = '/content/Annotations/Test Annotation.csv'
with open(filename, 'r') as csvfile:
    datareader = csv.reader(csvfile)
    for i,row in enumerate(datareader):
      if(i > 0):

        img_name = row[0]
        xmin = float(row[1])
        ymin = float(row[2])
        xmax = float(row[3])
        ymax = float(row[4])
        label = int(row[5]) - 1

        # since both train and test source images have same names but diff folders, we add 'test_' as prefix before copying for differentiation
        anno_dict["test_" + img_name] = (label, xmin, ymin, xmax, ymax)

Function to convert bounding box coordinates

• The given coordinates are in format (xmin, ymin) and (xmax, ymax) i.e. left bottom and top right corners of the box.
• For yolo to understand, they should be converted into the format (x,y) and (w,h) i.e. (x,y) being the center of the box and w,h denotes the width and height of the box.
• Since, yolo is pre-trained with this kind of annotations for coco images, we need to follow the same format before feeding it.

def to_coco_bbox(xmin, ymin, xmax, ymax, image_height, image_width):
  
  # calculate center, width and height of bbox
  x = (xmin + xmax)/2
  y = (ymin + ymax)/2
  w = (xmax - xmin)
  h = (ymax - ymin)

  # normalize wrt image size
  x /= image_width
  y /= image_height
  w /= image_width
  h /= image_height

  return x,y,w,h

# ----------------------------------------------------------------
# Create a 'data' folder structure as follows to feed yolo model.
# ----------------------------------------------------------------
# data
#   +-- images
#         +-- test
#         +-- train
#         +-- val
#   +-- labels
#         +-- test
#         +-- train
#         +-- val
# ----------------------------------------------------------------

def create_folder_structure():
  if os.path.exists('data'): # if folder already exists, delete it.
    shutil.rmtree('data')

  for folder in ['images', 'labels']:
      for split in ['train', 'val', 'test']:
          os.makedirs(f'data/{folder}/{split}')

# creates a data yaml file for yolo
def create_data_yaml(labels):

  fp = open('cars_data.yaml', 'w')

  fp.write("# Dataset paths relative to the yolov5 folder \n")
  fp.write("train: ../data/images/train \n")
  fp.write("val:   ../data/images/val \n")
  fp.write("test:  ../data/images/test \n")

  fp.write("\n# Number of classes\n")
  fp.write("nc: " + str(len(labels)) + "\n")

  fp.write("names: " + str(labels))

  fp.close()

# This function does the data preparation as follows:
#
#  1. Create a folder structure as mentioned above.
#  2. For each class, combine all the source data images from both train and test folders.
#  3. Split the images and annotations into the given (train,val,test) ratio.
#  4. After splitting, write the images and labels into the corresponding train, val and test sub folders for the model to use.
#  5. At the end, print the overall split stats, create a data yaml for for yolo and return the class wise split info. 
#
#  Note: We can use 'first_n_classes' to load only the first few number of classes data. 
#        For instance, we can load only the first 5 classes of data and create a corresponding baseline model for any future estimations.
#        It's default value is 196 which covers all the data.

def prepare_data_yolov5(train_split=0.7, val_split=0.2, test_split=0.1, first_n_classes=196):
  
  class_split_info = {}

  create_folder_structure()

  all_cars = list(car_names_dict.values())

  # iterate over each class
  for current_car in all_cars[0:first_n_classes]:

    train_path = '/content/Car Images/Train Images/' + current_car
    test_path = '/content/Car Images/Test Images/' + current_car

    train_images_names = os.listdir(train_path)
    test_images_names = os.listdir(test_path)

    total_size = len(train_images_names) + len(test_images_names)

    # calculate new (train,val,split) sizes
    new_train_size = int(total_size * train_split)
    new_test_size = int(total_size * test_split)
    new_val_size = total_size - (new_train_size + new_test_size)
    
    # store split info for the current class
    class_split_info[current_car] = {"total_size": total_size, "train_size": new_train_size, "val_size": new_val_size, "test_size": new_test_size}

    # combine all the image names of the current class
    # since both train and test source images have same names but diff folders, we add 'test_' as prefix before copying for differentiation 
    all_image_names = train_images_names + ["test_" + i for i in test_images_names]

    for i,image_name in enumerate(all_image_names):

      # determine the destination directory 
      if (i < new_train_size):
        dst_dir = '/content/data/images/train'
      
      elif (i < new_train_size + new_val_size):
        dst_dir = '/content/data/images/val'
      
      else:
        dst_dir = '/content/data/images/test'
      

      # copy image from source to destination
      if("test_" in image_name):
        src = test_path + "/" + image_name.replace("test_", "") # remove 'test_' to determine the source image path of test images
      else:
        src = train_path + "/" + image_name

      dst = dst_dir + "/" + image_name
      
      shutil.copy(src, dst)


      # create a new label file for the corresponding image and copy it's bbox coordinates
      image = cv2.imread(dst)
      image_height, image_width = image.shape[0], image.shape[1]
      
      label, xmin, ymin, xmax, ymax = anno_dict[image_name]
      
      x,y,w,h = to_coco_bbox(xmin, ymin, xmax, ymax, image_height, image_width)
      anno = str(label) +' '+ str(x) +' '+ str(y) +' '+ str(w) +' '+ str(h)

      dst = dst.replace("images", "labels")
      dst = dst.replace("jpg", "txt")
            
      fp = open(dst, 'w')
      fp.write(anno)
      fp.close()

  class_split_info = pd.DataFrame(class_split_info).T
  
  print('     Total size:', class_split_info['total_size'].sum())
  print('     Train size:', class_split_info['train_size'].sum())
  print('Validation size:', class_split_info['val_size'].sum())
  print('      Test size:', class_split_info['test_size'].sum())

  labels = list(class_split_info.index)
  create_data_yaml(labels)

  return class_split_info

Base Model with only 3 classes

• Checking if yolov5 performs well at the basic level with only 3 classes.

prepare_data_yolov5(train_split=0.7, val_split=0.2, test_split=0.1, first_n_classes=3)

     Total size: 239
     Train size: 166
Validation size: 51
      Test size: 22

	total_size	train_size	val_size	test_size
AM General Hummer SUV 2000	89	62	19	8
Acura RL Sedan 2012	64	44	14	6
Acura TL Sedan 2012	86	60	18	8

!cat cars_data.yaml

# Dataset paths relative to the yolov5 folder 
train: ../data/images/train 
val:   ../data/images/val 
test:  ../data/images/test 

# Number of classes
nc: 3
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012']

Train the model

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 100

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 132MB/s]
Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:03<00:00, 3.85MB/s]

Overriding model.yaml nc=80 with nc=3

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1     21576  models.yolo.Detect                      [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train... 166 images, 0 backgrounds, 0 corrupt: 100% 166/166 [00:00<00:00, 1640.42it/s]
train: New cache created: /content/data/labels/train.cache
val: Scanning /content/data/labels/val... 51 images, 0 backgrounds, 0 corrupt: 100% 51/51 [00:00<00:00, 797.98it/s]
val: New cache created: /content/data/labels/val.cache

AutoAnchor: 2.01 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp/labels.jpg... 
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       0/99       3.5G    0.07703    0.03158    0.02864         18        640: 100% 11/11 [00:07<00:00,  1.38it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:02<00:00,  1.44s/it]
                   all         51         51    0.00343          1     0.0388     0.0128

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       1/99      3.92G    0.05955     0.0308    0.02879         17        640: 100% 11/11 [00:04<00:00,  2.58it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.52it/s]
                   all         51         51     0.0883      0.546      0.144     0.0533

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       2/99      3.92G    0.05658    0.02842    0.02837         13        640: 100% 11/11 [00:04<00:00,  2.72it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.88it/s]
                   all         51         51      0.203      0.441      0.188     0.0597

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       3/99      3.92G    0.04263    0.02294    0.02404         13        640: 100% 11/11 [00:04<00:00,  2.70it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.44it/s]
                   all         51         51      0.234      0.686      0.302      0.123

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       4/99      3.92G    0.04085    0.02088    0.02305         18        640: 100% 11/11 [00:05<00:00,  2.00it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.43it/s]
                   all         51         51      0.389      0.588      0.438      0.246

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       5/99      3.92G    0.04393    0.01751    0.02255         13        640: 100% 11/11 [00:04<00:00,  2.59it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.20it/s]
                   all         51         51      0.406      0.507      0.494      0.222

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       6/99      3.92G     0.0431    0.01758    0.02459         19        640: 100% 11/11 [00:04<00:00,  2.38it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.13it/s]
                   all         51         51      0.392      0.769      0.557      0.275

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       7/99      3.92G    0.03748    0.01556    0.02154         15        640: 100% 11/11 [00:03<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.44it/s]
                   all         51         51      0.335      0.646      0.487      0.236

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       8/99      3.92G    0.04279    0.01495    0.02107         16        640: 100% 11/11 [00:04<00:00,  2.72it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.24it/s]
                   all         51         51       0.41      0.615      0.535      0.205

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       9/99      3.92G    0.04217    0.01406    0.01888         15        640: 100% 11/11 [00:04<00:00,  2.40it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.20it/s]
                   all         51         51      0.395      0.811      0.481      0.205

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      10/99      3.92G    0.03359    0.01501    0.01887         19        640: 100% 11/11 [00:03<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.93it/s]
                   all         51         51      0.417      0.803      0.612      0.257

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      11/99      3.92G    0.04055     0.0141    0.02151         16        640: 100% 11/11 [00:04<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.31it/s]
                   all         51         51      0.301      0.762      0.522      0.289

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      12/99      3.92G    0.03409    0.01337     0.0162         18        640: 100% 11/11 [00:03<00:00,  2.78it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.21it/s]
                   all         51         51      0.361      0.676      0.591      0.329

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      13/99      3.92G     0.0362    0.01372    0.01694         12        640: 100% 11/11 [00:04<00:00,  2.72it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.25it/s]
                   all         51         51      0.637      0.937      0.735      0.447

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      14/99      3.92G    0.03136    0.01341    0.01585         18        640: 100% 11/11 [00:04<00:00,  2.74it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.92it/s]
                   all         51         51      0.597      0.759      0.662      0.319

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      15/99      3.92G     0.0354    0.01216    0.01815         14        640: 100% 11/11 [00:04<00:00,  2.47it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.95it/s]
                   all         51         51      0.432      0.782       0.64       0.38

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      16/99      3.92G    0.03065    0.01373    0.01694         18        640: 100% 11/11 [00:04<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.35it/s]
                   all         51         51      0.536      0.892      0.715      0.481

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      17/99      3.92G    0.03198    0.01356    0.01747         17        640: 100% 11/11 [00:03<00:00,  2.76it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.34it/s]
                   all         51         51      0.579      0.905      0.731      0.416

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      18/99      3.92G    0.02559    0.01262    0.01623         19        640: 100% 11/11 [00:04<00:00,  2.48it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.17it/s]
                   all         51         51      0.611      0.903      0.709      0.364

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      19/99      3.92G    0.03115    0.01248    0.01778         17        640: 100% 11/11 [00:04<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.06it/s]
                   all         51         51      0.334      0.828       0.52      0.322

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      20/99      3.92G    0.02338    0.01198    0.01369         18        640: 100% 11/11 [00:04<00:00,  2.64it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.51it/s]
                   all         51         51      0.588      0.911      0.695      0.435

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      21/99      3.92G    0.03362    0.01195    0.01598         19        640: 100% 11/11 [00:04<00:00,  2.29it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.21it/s]
                   all         51         51      0.541      0.929      0.719       0.37

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      22/99      3.92G    0.02841    0.01153    0.01595         15        640: 100% 11/11 [00:04<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.52it/s]
                   all         51         51       0.62      0.887      0.751      0.488

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      23/99      3.92G    0.02599    0.01239     0.0156         22        640: 100% 11/11 [00:04<00:00,  2.35it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.33it/s]
                   all         51         51      0.539      0.952      0.694      0.473

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      24/99      3.92G    0.02177    0.01165    0.01254         22        640: 100% 11/11 [00:04<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.09it/s]
                   all         51         51      0.644      0.948      0.719      0.419

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      25/99      3.92G      0.029    0.01061     0.0153         12        640: 100% 11/11 [00:04<00:00,  2.62it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.25it/s]
                   all         51         51      0.663      0.935      0.724      0.427

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      26/99      3.92G    0.02141    0.01093    0.01454         18        640: 100% 11/11 [00:04<00:00,  2.59it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.33it/s]
                   all         51         51      0.641      0.921      0.728      0.386

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      27/99      3.92G    0.02653     0.0113    0.01761         17        640: 100% 11/11 [00:04<00:00,  2.44it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.12it/s]
                   all         51         51      0.637          1      0.735      0.499

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      28/99      3.92G    0.02154    0.01117    0.01317         22        640: 100% 11/11 [00:04<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.25it/s]
                   all         51         51       0.66          1      0.749      0.351

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      29/99      3.92G    0.02292    0.01072    0.01364         13        640: 100% 11/11 [00:04<00:00,  2.59it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.98it/s]
                   all         51         51       0.65          1      0.751      0.491

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      30/99      3.92G    0.01964    0.01015    0.01249         18        640: 100% 11/11 [00:04<00:00,  2.41it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.18it/s]
                   all         51         51      0.482          1      0.654      0.446

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      31/99      3.92G    0.02858    0.01147    0.01618         14        640: 100% 11/11 [00:04<00:00,  2.37it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.26it/s]
                   all         51         51      0.647      0.976       0.76      0.538

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      32/99      3.92G    0.02024    0.01089    0.01364         18        640: 100% 11/11 [00:03<00:00,  2.90it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.30it/s]
                   all         51         51      0.659      0.999      0.774      0.526

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      33/99      3.92G    0.02536    0.01162    0.01888         19        640: 100% 11/11 [00:04<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.19it/s]
                   all         51         51      0.652      0.978      0.754      0.569

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      34/99      3.92G    0.02001    0.01102    0.01702         18        640: 100% 11/11 [00:03<00:00,  2.76it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.51it/s]
                   all         51         51      0.656      0.993      0.755      0.561

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      35/99      3.92G    0.02607    0.00955    0.01812          9        640: 100% 11/11 [00:04<00:00,  2.42it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.31it/s]
                   all         51         51       0.66      0.983      0.743      0.565

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      36/99      3.92G    0.01942    0.01039    0.01205         21        640: 100% 11/11 [00:04<00:00,  2.40it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.33it/s]
                   all         51         51      0.626      0.996      0.738      0.493

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      37/99      3.92G    0.01944    0.01017    0.01379         18        640: 100% 11/11 [00:04<00:00,  2.62it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.25it/s]
                   all         51         51      0.618      0.986      0.729      0.531

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      38/99      3.92G    0.01982    0.01017    0.01344         13        640: 100% 11/11 [00:04<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.39it/s]
                   all         51         51      0.628      0.966      0.743      0.576

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      39/99      3.92G    0.01859    0.01025    0.01378         13        640: 100% 11/11 [00:04<00:00,  2.37it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.39it/s]
                   all         51         51      0.654          1      0.709      0.505

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      40/99      3.92G    0.02023     0.0106    0.01404         21        640: 100% 11/11 [00:04<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.44it/s]
                   all         51         51      0.646          1      0.717      0.531

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      41/99      3.92G    0.02007    0.01031    0.01388         15        640: 100% 11/11 [00:04<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.50it/s]
                   all         51         51      0.644          1      0.722      0.469

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      42/99      3.92G    0.01528    0.00979    0.01234         15        640: 100% 11/11 [00:04<00:00,  2.28it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.06it/s]
                   all         51         51      0.655      0.909      0.707      0.279

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      43/99      3.92G    0.02154   0.009997    0.01723         17        640: 100% 11/11 [00:04<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.38it/s]
                   all         51         51      0.648          1      0.746      0.488

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      44/99      3.92G    0.01707    0.01075    0.01305         19        640: 100% 11/11 [00:04<00:00,  2.30it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.34it/s]
                   all         51         51      0.639          1      0.768      0.565

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      45/99      3.92G    0.01666   0.009671    0.01308         16        640: 100% 11/11 [00:04<00:00,  2.39it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.34it/s]
                   all         51         51      0.647      0.982      0.753      0.566

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      46/99      3.92G    0.01616   0.009322    0.01288         20        640: 100% 11/11 [00:04<00:00,  2.36it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.28it/s]
                   all         51         51      0.643      0.986      0.775      0.518

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      47/99      3.92G    0.01557   0.008975    0.01183         18        640: 100% 11/11 [00:04<00:00,  2.61it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.23it/s]
                   all         51         51      0.647      0.992      0.754      0.515

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      48/99      3.92G    0.02382   0.009549    0.01659         15        640: 100% 11/11 [00:04<00:00,  2.30it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.39it/s]
                   all         51         51      0.639      0.993      0.745      0.587

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      49/99      3.92G     0.0177    0.01052    0.01208         23        640: 100% 11/11 [00:04<00:00,  2.71it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.59it/s]
                   all         51         51      0.699      0.903      0.763      0.501

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      50/99      3.92G    0.01709   0.009567    0.01274         13        640: 100% 11/11 [00:04<00:00,  2.46it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.08it/s]
                   all         51         51      0.646      0.976      0.728       0.54

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      51/99      3.92G    0.01899    0.01023     0.0134         17        640: 100% 11/11 [00:04<00:00,  2.47it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.12it/s]
                   all         51         51      0.645       0.97      0.747      0.518

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      52/99      3.92G     0.0193   0.009712    0.01364         18        640: 100% 11/11 [00:03<00:00,  2.78it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.61it/s]
                   all         51         51      0.638      0.986       0.75      0.531

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      53/99      3.92G     0.0196   0.009562     0.0141         17        640: 100% 11/11 [00:04<00:00,  2.56it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.21it/s]
                   all         51         51       0.64          1       0.76      0.596

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      54/99      3.92G    0.01749   0.009739    0.01362         18        640: 100% 11/11 [00:04<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.49it/s]
                   all         51         51      0.643      0.999      0.751      0.572

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      55/99      3.92G    0.01623    0.00965    0.01286         18        640: 100% 11/11 [00:04<00:00,  2.39it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.26it/s]
                   all         51         51      0.647          1       0.78       0.61

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      56/99      3.92G    0.01491    0.00888    0.01255         17        640: 100% 11/11 [00:04<00:00,  2.73it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.09it/s]
                   all         51         51      0.643          1      0.788      0.595

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      57/99      3.92G    0.01492   0.008821    0.01061         16        640: 100% 11/11 [00:04<00:00,  2.24it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  1.87it/s]
                   all         51         51      0.639          1      0.806       0.63

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      58/99      3.92G    0.01736   0.009036    0.01223         17        640: 100% 11/11 [00:04<00:00,  2.46it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.43it/s]
                   all         51         51      0.647          1      0.804      0.627

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      59/99      3.92G    0.01458   0.008722     0.0145         14        640: 100% 11/11 [00:04<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.13it/s]
                   all         51         51      0.654          1       0.79      0.635

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      60/99      3.92G    0.02227   0.009889    0.01762         22        640: 100% 11/11 [00:04<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.05it/s]
                   all         51         51      0.654          1      0.805      0.548

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      61/99      3.92G    0.01459   0.009262    0.01102         17        640: 100% 11/11 [00:04<00:00,  2.52it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.25it/s]
                   all         51         51      0.655          1      0.797      0.622

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      62/99      3.92G    0.01501   0.009149    0.01219         22        640: 100% 11/11 [00:04<00:00,  2.43it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.36it/s]
                   all         51         51      0.651          1      0.805      0.628

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      63/99      3.92G    0.01845   0.008781    0.01444         17        640: 100% 11/11 [00:04<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.09it/s]
                   all         51         51      0.651          1      0.807      0.655

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      64/99      3.92G    0.01224   0.008865     0.0111         13        640: 100% 11/11 [00:03<00:00,  2.79it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.33it/s]
                   all         51         51      0.677      0.905      0.833      0.657

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      65/99      3.92G    0.01962   0.009397    0.01721         16        640: 100% 11/11 [00:04<00:00,  2.36it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.29it/s]
                   all         51         51      0.674      0.905      0.837      0.653

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      66/99      3.92G    0.01833   0.008478    0.02125         10        640: 100% 11/11 [00:04<00:00,  2.53it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.31it/s]
                   all         51         51      0.693      0.905      0.847      0.673

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      67/99      3.92G     0.0135   0.008743    0.01242         14        640: 100% 11/11 [00:04<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.37it/s]
                   all         51         51      0.742      0.894      0.849      0.716

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      68/99      3.92G    0.01257   0.009206   0.009883         19        640: 100% 11/11 [00:04<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.77it/s]
                   all         51         51      0.767      0.881      0.854      0.701

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      69/99      3.92G    0.01199   0.008297     0.0106         18        640: 100% 11/11 [00:04<00:00,  2.26it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.09it/s]
                   all         51         51      0.801      0.875      0.855      0.697

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      70/99      3.92G    0.01359   0.008574    0.01203         17        640: 100% 11/11 [00:04<00:00,  2.36it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.06it/s]
                   all         51         51      0.817       0.86      0.854      0.681

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      71/99      3.92G    0.01537   0.008887    0.01144         15        640: 100% 11/11 [00:04<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:01<00:00,  2.00it/s]
                   all         51         51      0.774      0.868      0.861      0.688

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      72/99      3.92G    0.01884   0.007933    0.01095         14        640: 100% 11/11 [00:04<00:00,  2.25it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.38it/s]
                   all         51         51      0.766      0.892      0.865      0.684

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      73/99      3.92G    0.01666   0.008508    0.01154         15        640: 100% 11/11 [00:04<00:00,  2.74it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.58it/s]
                   all         51         51      0.771      0.952      0.886      0.719

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      74/99      3.92G    0.01504   0.008568    0.01107         20        640: 100% 11/11 [00:04<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.31it/s]
                   all         51         51      0.781      0.983      0.905      0.682

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      75/99      3.92G     0.0149   0.008636    0.01038         14        640: 100% 11/11 [00:04<00:00,  2.37it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.32it/s]
                   all         51         51      0.787      0.929      0.922       0.77

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      76/99      3.92G    0.01386   0.008383    0.01052         18        640: 100% 11/11 [00:04<00:00,  2.56it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.12it/s]
                   all         51         51      0.862      0.881      0.928      0.759

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      77/99      3.92G    0.01447   0.007769    0.01299         17        640: 100% 11/11 [00:04<00:00,  2.44it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.08it/s]
                   all         51         51      0.875      0.915      0.938      0.768

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      78/99      3.92G    0.01238   0.008078   0.009398         16        640: 100% 11/11 [00:03<00:00,  2.77it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.20it/s]
                   all         51         51      0.886      0.918       0.94      0.784

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      79/99      3.92G    0.01526   0.007752    0.01021         19        640: 100% 11/11 [00:04<00:00,  2.59it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.47it/s]
                   all         51         51       0.88        0.9      0.936      0.774

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      80/99      3.92G    0.01397   0.007937   0.009177         15        640: 100% 11/11 [00:04<00:00,  2.21it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.06it/s]
                   all         51         51      0.905      0.888      0.938      0.775

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      81/99      3.92G    0.01237   0.008124   0.008044         14        640: 100% 11/11 [00:04<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.02it/s]
                   all         51         51      0.856      0.896      0.926       0.77

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      82/99      3.92G    0.01702   0.009267    0.01061         18        640: 100% 11/11 [00:04<00:00,  2.59it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.20it/s]
                   all         51         51      0.873      0.914      0.941      0.785

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      83/99      3.92G    0.01448   0.008351     0.0135         15        640: 100% 11/11 [00:04<00:00,  2.61it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.09it/s]
                   all         51         51      0.915       0.89      0.942      0.773

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      84/99      3.92G    0.01297   0.008126   0.008228         16        640: 100% 11/11 [00:05<00:00,  2.17it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.26it/s]
                   all         51         51      0.919      0.883       0.94      0.791

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      85/99      3.92G     0.0156   0.008838    0.01007         18        640: 100% 11/11 [00:04<00:00,  2.57it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.40it/s]
                   all         51         51      0.904      0.893      0.938      0.793

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      86/99      3.92G    0.01004   0.008031   0.007856         19        640: 100% 11/11 [00:04<00:00,  2.34it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.22it/s]
                   all         51         51      0.935      0.894      0.943      0.799

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      87/99      3.92G    0.01315   0.008974   0.008683         16        640: 100% 11/11 [00:04<00:00,  2.64it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.30it/s]
                   all         51         51      0.906      0.881      0.943       0.79

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      88/99      3.92G    0.01184   0.008394   0.007181         10        640: 100% 11/11 [00:04<00:00,  2.43it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.10it/s]
                   all         51         51      0.847      0.924      0.943      0.803

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      89/99      3.92G     0.0141   0.008162   0.007592         21        640: 100% 11/11 [00:04<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.19it/s]
                   all         51         51       0.92      0.861      0.944       0.81

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      90/99      3.92G    0.01321   0.007876   0.007682         18        640: 100% 11/11 [00:03<00:00,  2.96it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.78it/s]
                   all         51         51      0.903      0.888      0.946      0.794

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      91/99      3.92G    0.01203   0.008307   0.006921         23        640: 100% 11/11 [00:04<00:00,  2.47it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.16it/s]
                   all         51         51      0.909      0.892      0.949      0.804

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      92/99      3.92G    0.01278   0.008209   0.007641         19        640: 100% 11/11 [00:04<00:00,  2.64it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.10it/s]
                   all         51         51      0.918      0.893      0.949      0.809

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      93/99      3.92G    0.01306   0.008016   0.008152         14        640: 100% 11/11 [00:04<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.00it/s]
                   all         51         51      0.926      0.906       0.95      0.798

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      94/99      3.92G   0.009752   0.006873   0.006647         17        640: 100% 11/11 [00:04<00:00,  2.52it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.73it/s]
                   all         51         51      0.926       0.91       0.95      0.796

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      95/99      3.92G    0.01323    0.00753   0.008249         16        640: 100% 11/11 [00:04<00:00,  2.40it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.19it/s]
                   all         51         51      0.924       0.91      0.951      0.805

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      96/99      3.92G    0.01521   0.007865   0.007007         18        640: 100% 11/11 [00:04<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.19it/s]
                   all         51         51      0.926      0.909      0.946        0.8

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      97/99      3.92G   0.008978   0.007476   0.005518         17        640: 100% 11/11 [00:04<00:00,  2.38it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.20it/s]
                   all         51         51      0.925       0.91      0.942        0.8

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      98/99      3.92G    0.01084   0.007815   0.005473         17        640: 100% 11/11 [00:04<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.38it/s]
                   all         51         51      0.927       0.91      0.941      0.804

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      99/99      3.92G   0.009798    0.00795   0.005504         17        640: 100% 11/11 [00:05<00:00,  2.10it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.19it/s]
                   all         51         51       0.93       0.91      0.945      0.804

100 epochs completed in 0.175 hours.
Optimizer stripped from yolov5/runs/train/exp/weights/last.pt, 14.4MB
Optimizer stripped from yolov5/runs/train/exp/weights/best.pt, 14.4MB

Validating yolov5/runs/train/exp/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [00:00<00:00,  2.10it/s]
                   all         51         51       0.92      0.861      0.944       0.81
AM General Hummer SUV 2000         51         19          1      0.991      0.995      0.811
   Acura RL Sedan 2012         51         14      0.825      0.786      0.904      0.836
   Acura TL Sedan 2012         51         18      0.935      0.806      0.934      0.784
Results saved to yolov5/runs/train/exp

Inferences from above exercise

• We achieved a precision of 92% and mAP50 of 94% indicating that the base model is performing well.
• The recall and mAP50 are high for the images with more number of training images.
• The recommended split for yolov5 is (train: 70, val: 20, test: 10), which can give more images to train.
• This gives us some confidence to apply the same model for bigger dataset.

Image(filename='/content/yolov5/runs/train/exp/results.png')

Predict some of the test images

!python yolov5/detect.py --source '/content/data/images/test' --weights '/content/yolov5/runs/train/exp/weights/best.pt' --img-size 320

detect: weights=['/content/yolov5/runs/train/exp/weights/best.pt'], source=/content/data/images/test, data=yolov5/data/coco128.yaml, imgsz=[320, 320], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

Fusing layers... 
Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
image 1/22 /content/data/images/test/test_00347.jpg: 128x320 1 Acura RL Sedan 2012, 1 Acura TL Sedan 2012, 11.0ms
image 2/22 /content/data/images/test/test_00397.jpg: 224x320 1 Acura RL Sedan 2012, 11.3ms
image 3/22 /content/data/images/test/test_00684.jpg: 192x320 1 AM General Hummer SUV 2000, 11.7ms
image 4/22 /content/data/images/test/test_00913.jpg: 224x320 1 Acura TL Sedan 2012, 8.2ms
image 5/22 /content/data/images/test/test_01117.jpg: 256x320 1 AM General Hummer SUV 2000, 12.2ms
image 6/22 /content/data/images/test/test_01946.jpg: 160x320 1 AM General Hummer SUV 2000, 11.2ms
image 7/22 /content/data/images/test/test_02035.jpg: 224x320 1 Acura TL Sedan 2012, 8.5ms
image 8/22 /content/data/images/test/test_02602.jpg: 224x320 1 AM General Hummer SUV 2000, 7.8ms
image 9/22 /content/data/images/test/test_03174.jpg: 256x320 1 Acura TL Sedan 2012, 8.0ms
image 10/22 /content/data/images/test/test_03193.jpg: 320x288 1 AM General Hummer SUV 2000, 12.1ms
image 11/22 /content/data/images/test/test_03246.jpg: 160x320 1 AM General Hummer SUV 2000, 12.1ms
image 12/22 /content/data/images/test/test_03306.jpg: 224x320 1 Acura RL Sedan 2012, 8.2ms
image 13/22 /content/data/images/test/test_03311.jpg: 256x320 1 Acura TL Sedan 2012, 8.0ms
image 14/22 /content/data/images/test/test_04427.jpg: 256x320 1 Acura TL Sedan 2012, 7.5ms
image 15/22 /content/data/images/test/test_04757.jpg: 256x320 1 Acura TL Sedan 2012, 7.6ms
image 16/22 /content/data/images/test/test_04920.jpg: 224x320 1 Acura RL Sedan 2012, 7.8ms
image 17/22 /content/data/images/test/test_06057.jpg: 288x320 1 Acura RL Sedan 2012, 11.4ms
image 18/22 /content/data/images/test/test_06250.jpg: 224x320 1 Acura RL Sedan 2012, 8.1ms
image 19/22 /content/data/images/test/test_06478.jpg: 224x320 1 Acura TL Sedan 2012, 7.4ms
image 20/22 /content/data/images/test/test_07255.jpg: 128x320 1 Acura RL Sedan 2012, 1 Acura TL Sedan 2012, 7.7ms
image 21/22 /content/data/images/test/test_07917.jpg: 192x320 1 AM General Hummer SUV 2000, 8.1ms
image 22/22 /content/data/images/test/test_08035.jpg: 224x320 1 AM General Hummer SUV 2000, 8.5ms
Speed: 0.2ms pre-process, 9.3ms inference, 0.9ms NMS per image at shape (1, 3, 320, 320)
Results saved to yolov5/runs/detect/exp2

Evaluation on test images

!python yolov5/val.py --batch 32 --data cars_data.yaml --weights '/content/yolov5/runs/train/exp/weights/best.pt' --task test --img-size 320

val: data=cars_data.yaml, weights=['/content/yolov5/runs/train/exp/weights/best.pt'], batch_size=32, imgsz=320, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=yolov5/runs/val, name=exp, exist_ok=False, half=False, dnn=False
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

Fusing layers... 
Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
test: Scanning /content/data/labels/test... 22 images, 0 backgrounds, 0 corrupt: 100% 22/22 [00:00<00:00, 403.79it/s]
test: New cache created: /content/data/labels/test.cache
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  1.89it/s]
                   all         22         22      0.727      0.919      0.846      0.738
AM General Hummer SUV 2000         22          8      0.925          1      0.995      0.859
   Acura RL Sedan 2012         22          6      0.589          1      0.713      0.648
   Acura TL Sedan 2012         22          8      0.668      0.756      0.831      0.706
Speed: 0.1ms pre-process, 2.8ms inference, 2.1ms NMS per image at shape (32, 3, 320, 320)
Results saved to yolov5/runs/val/exp2

Inferences

• The overall recall is 92% and mAP50 being 85% for the test images.
• Indicates that model is performing well enough.

Image(filename='/content/yolov5/runs/detect/exp2/test_00397.jpg', width=500)

Before jumping to the final model. Experiment with some parameters to reduce training time

prepare_data_yolov5(train_split=0.7, val_split=0.2, test_split=0.1, first_n_classes=196) # select all 196 classes

     Total size: 16185
     Train size: 11226
Validation size: 3435
      Test size: 1524

	total_size	train_size	val_size	test_size
AM General Hummer SUV 2000	89	62	19	8
Acura RL Sedan 2012	64	44	14	6
Acura TL Sedan 2012	86	60	18	8
Acura TL Type-S 2008	84	58	18	8
Acura TSX Sedan 2012	81	56	17	8
...	...	...	...	...
Volkswagen Beetle Hatchback 2012	85	59	18	8
Volvo C30 Hatchback 2012	83	58	17	8
Volvo 240 Sedan 1993	91	63	19	9
Volvo XC90 SUV 2007	86	60	18	8
smart fortwo Convertible 2012	80	56	16	8

196 rows × 4 columns

!cat cars_data.yaml

# Dataset paths relative to the yolov5 folder 
train: ../data/images/train 
val:   ../data/images/val 
test:  ../data/images/test 

# Number of classes
nc: 196
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C-V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']

Experiments to better utilise the resources

There's always a trade off between the available GPU RAM (12.68 GB) and training time. Hence, playing with the following fields to reduce the training time as much as possible, but not consume too much RAM.

• Caching the images.
• Image size - [640 (default), 320]
• Batch size - [16 (default), 32, 64]
• Workers (No of parallel threads) - [8 (default), 16, 32]

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: 8.9GB RAM required, 8.7/12.7GB available, not caching images ⚠️
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (2.7GB ram): 100% 3435/3435 [00:35<00:00, 97.13it/s]

AutoAnchor: 2.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp7/labels.jpg... 
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp7
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1      3.78G    0.03354    0.01638    0.09328         29        640: 100% 702/702 [08:47<00:00,  1.33it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:38<00:00,  2.81it/s]
                   all       3435       3435    0.00436      0.986    0.00868    0.00593

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1      8.87G    0.01955    0.01062    0.09388         41        640:  19% 132/702 [01:30<06:28,  1.47it/s]
Traceback (most recent call last):
  File "yolov5/train.py", line 633, in <module>
    main(opt)
  File "yolov5/train.py", line 527, in main
    train(opt.hyp, opt, device, callbacks)
  File "yolov5/train.py", line 308, in train
    pred = model(imgs)  # forward
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/yolov5/models/yolo.py", line 209, in forward
    return self._forward_once(x, profile, visualize)  # single-scale inference, train
  File "/content/yolov5/models/yolo.py", line 121, in _forward_once
    x = m(x)  # run
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/yolov5/models/common.py", line 168, in forward
    return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py", line 204, in forward
    input = module(input)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/yolov5/models/common.py", line 121, in forward
    return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/yolov5/models/common.py", line 57, in forward
    return self.act(self.bn(self.conv(x)))
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py", line 463, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py", line 460, in _conv_forward
    self.padding, self.dilation, self.groups)
KeyboardInterrupt

The above run almost crashed the RAM. Hence interrupted and going for the next parameters.

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 # reduce image size

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=16, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:14<00:00, 149.88it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:35<00:00, 97.68it/s] 

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp8/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp8
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1       1.1G    0.04281    0.01722     0.1226         29        320: 100% 702/702 [01:44<00:00,  6.72it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:21<00:00,  5.12it/s]
                   all       3435       3435    0.00487      0.997    0.00813    0.00636

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1       2.1G     0.0225    0.01122     0.1203         32        320: 100% 702/702 [01:39<00:00,  7.03it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:20<00:00,  5.16it/s]
                   all       3435       3435      0.005      0.999    0.00791    0.00687

2 epochs completed in 0.070 hours.
Optimizer stripped from yolov5/runs/train/exp8/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp8/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp8/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:22<00:00,  4.72it/s]
                   all       3435       3435      0.005      0.999    0.00791    0.00687
AM General Hummer SUV 2000       3435         19    0.00547          1    0.00655    0.00523
   Acura RL Sedan 2012       3435         14    0.00401          1     0.0125      0.011
   Acura TL Sedan 2012       3435         18    0.00519          1    0.00681    0.00608
  Acura TL Type-S 2008       3435         18    0.00508          1    0.00662    0.00592
  Acura TSX Sedan 2012       3435         17    0.00486          1    0.00792    0.00721
Acura Integra Type R 2001       3435         19    0.00512      0.947    0.00535    0.00453
Acura ZDX Hatchback 2012       3435         17    0.00485          1    0.00516    0.00441
Aston Martin V8 Vantage Convertible 2012       3435         19     0.0055          1    0.00736     0.0067
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00481          1    0.00524    0.00416
Aston Martin Virage Convertible 2012       3435         14    0.00399          1     0.0071    0.00647
Aston Martin Virage Coupe 2012       3435         16    0.00458          1    0.00743    0.00654
Audi RS 4 Convertible 2008       3435         15    0.00428          1    0.00529    0.00474
    Audi A5 Coupe 2012       3435         17    0.00489          1    0.00531    0.00449
   Audi TTS Coupe 2012       3435         18    0.00511          1    0.00594    0.00535
    Audi R8 Coupe 2012       3435         18    0.00511          1    0.00834    0.00718
    Audi V8 Sedan 1994       3435         19    0.00536          1      0.007    0.00622
   Audi 100 Sedan 1994       3435         17    0.00484          1     0.0078    0.00695
   Audi 100 Wagon 1994       3435         18    0.00514          1    0.00525    0.00416
Audi TT Hatchback 2011       3435         17     0.0048          1    0.00675    0.00617
    Audi S6 Sedan 2011       3435         19    0.00543          1     0.0159      0.015
Audi S5 Convertible 2012       3435         18     0.0049      0.944    0.00614    0.00564
    Audi S5 Coupe 2012       3435         18    0.00518          1    0.00722    0.00567
    Audi S4 Sedan 2012       3435         17    0.00485          1     0.0062    0.00528
    Audi S4 Sedan 2007       3435         19    0.00538          1    0.00727    0.00675
 Audi TT RS Coupe 2012       3435         17    0.00491          1    0.00546    0.00463
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00424          1     0.0114    0.00932
BMW 1 Series Convertible 2012       3435         15     0.0043          1     0.0091    0.00791
BMW 1 Series Coupe 2012       3435         17    0.00488          1    0.00729    0.00594
BMW 3 Series Sedan 2012       3435         18    0.00482      0.944     0.0352     0.0313
BMW 3 Series Wagon 2012       3435         17    0.00486          1    0.00503    0.00399
BMW 6 Series Convertible 2007       3435         19    0.00536          1    0.00582     0.0052
       BMW X5 SUV 2007       3435         17    0.00489          1     0.0106    0.00915
       BMW X6 SUV 2012       3435         18     0.0051          1     0.0103    0.00971
     BMW M3 Coupe 2012       3435         19    0.00545          1    0.00768    0.00702
     BMW M5 Sedan 2010       3435         17     0.0049          1     0.0082    0.00724
BMW M6 Convertible 2010       3435         17    0.00484          1    0.00627    0.00533
       BMW X3 SUV 2012       3435         17     0.0049          1     0.0118     0.0101
BMW Z4 Convertible 2012       3435         17    0.00492          1    0.00789    0.00757
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00435          1    0.00571    0.00493
Bentley Arnage Sedan 2009       3435         17    0.00491          1    0.00802    0.00714
Bentley Mulsanne Sedan 2011       3435         15    0.00432          1    0.00568     0.0049
Bentley Continental GT Coupe 2012       3435         15    0.00431          1     0.0198     0.0175
Bentley Continental GT Coupe 2007       3435         19    0.00513      0.947    0.00525    0.00458
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00539          1    0.00689    0.00632
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00404          1    0.00737    0.00637
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00533          1    0.00782    0.00652
   Buick Regal GS 2012       3435         14    0.00401          1    0.00518    0.00424
Buick Rainier SUV 2007       3435         18     0.0051          1    0.00817    0.00688
Buick Verano Sedan 2012       3435         16    0.00458          1    0.00632    0.00548
Buick Enclave SUV 2012       3435         18    0.00505          1     0.0132     0.0109
Cadillac CTS-V Sedan 2012       3435         18    0.00516          1    0.00614     0.0052
 Cadillac SRX SUV 2012       3435         17    0.00485          1    0.00632     0.0053
Cadillac Escalade EXT Crew Cab 2007       3435         19    0.00549          1    0.00677    0.00562
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00455          1    0.00733    0.00629
Chevrolet Corvette Convertible 2012       3435         17    0.00487          1    0.00901    0.00832
Chevrolet Corvette ZR1 2012       3435         19    0.00542          1    0.00562    0.00499
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00464          1    0.00532    0.00423
Chevrolet Traverse SUV 2012       3435         19    0.00529          1     0.0152     0.0132
Chevrolet Camaro Convertible 2012       3435         19    0.00541          1    0.00832    0.00749
 Chevrolet HHR SS 2010       3435         15     0.0043          1    0.00527    0.00477
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00611    0.00536
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00459          1    0.00852    0.00727
Chevrolet Sonic Sedan 2012       3435         19    0.00548          1     0.0119    0.00937
Chevrolet Express Cargo Van 2007       3435         13    0.00374          1    0.00909    0.00792
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00544          1    0.00738    0.00634
Chevrolet Cobalt SS 2010       3435         17    0.00488          1    0.00685    0.00542
Chevrolet Malibu Hybrid Sedan 2010       3435         17    0.00491          1    0.00588    0.00504
Chevrolet TrailBlazer SS 2009       3435         16    0.00456          1      0.006    0.00547
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00458          1    0.00634    0.00574
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00521          1      0.014     0.0131
Chevrolet Express Van 2007       3435         14    0.00401          1    0.00415    0.00357
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00534          1    0.00811    0.00682
Chevrolet Malibu Sedan 2007       3435         19    0.00544          1    0.00696     0.0059
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00537          1    0.00624    0.00523
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00546          1     0.0119     0.0107
Chrysler Aspen SUV 2009       3435         19    0.00537          1     0.0108    0.00934
Chrysler Sebring Convertible 2010       3435         17    0.00488          1    0.00527    0.00462
Chrysler Town and Country Minivan 2012       3435         16    0.00452          1    0.00755     0.0064
Chrysler 300 SRT-8 2010       3435         21    0.00604          1    0.00886    0.00784
Chrysler Crossfire Convertible 2008       3435         18    0.00517          1     0.0059    0.00496
Chrysler PT Cruiser Convertible 2008       3435         19    0.00545          1    0.00762    0.00627
Daewoo Nubira Wagon 2002       3435         19    0.00543          1    0.00703    0.00619
Dodge Caliber Wagon 2012       3435         17    0.00492          1    0.00603    0.00469
Dodge Caliber Wagon 2007       3435         18    0.00508          1    0.00587    0.00534
Dodge Caravan Minivan 1997       3435         19    0.00546          1    0.00638    0.00536
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00517          1    0.00676    0.00544
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00549          1    0.00721    0.00644
Dodge Sprinter Cargo Van 2009       3435         17    0.00487          1     0.0058    0.00491
Dodge Journey SUV 2012       3435         19    0.00542          1     0.0121     0.0109
Dodge Dakota Crew Cab 2010       3435         17    0.00485          1    0.00614    0.00515
Dodge Dakota Club Cab 2007       3435         17    0.00486          1    0.00566    0.00466
Dodge Magnum Wagon 2008       3435         16    0.00457          1    0.00581    0.00533
Dodge Challenger SRT8 2011       3435         17    0.00487          1    0.00553    0.00508
Dodge Durango SUV 2012       3435         19    0.00547          1    0.00808    0.00725
Dodge Durango SUV 2007       3435         19    0.00538          1    0.00641    0.00528
Dodge Charger Sedan 2012       3435         17    0.00483          1    0.00829    0.00742
Dodge Charger SRT-8 2009       3435         18    0.00513          1    0.00692    0.00605
Eagle Talon Hatchback 1998       3435         19    0.00545          1    0.00741     0.0068
  FIAT 500 Abarth 2012       3435         12    0.00342          1    0.00416     0.0031
FIAT 500 Convertible 2012       3435         15    0.00429          1    0.00465    0.00434
 Ferrari FF Coupe 2012       3435         18    0.00511          1    0.00738    0.00614
Ferrari California Convertible 2012       3435         17    0.00488          1    0.00741    0.00681
Ferrari 458 Italia Convertible 2012       3435         17    0.00486          1    0.00642    0.00517
Ferrari 458 Italia Coupe 2012       3435         18    0.00512          1    0.00528    0.00479
Fisker Karma Sedan 2012       3435         19     0.0054          1    0.00598    0.00501
Ford F-450 Super Duty Crew Cab 2012       3435         17    0.00481          1    0.00923    0.00794
Ford Mustang Convertible 2007       3435         19    0.00547          1      0.007     0.0056
Ford Freestar Minivan 2007       3435         19    0.00543          1    0.00622    0.00551
Ford Expedition EL SUV 2009       3435         19    0.00542          1    0.00629    0.00573
    Ford Edge SUV 2012       3435         18    0.00511          1     0.0142     0.0119
Ford Ranger SuperCab 2011       3435         18     0.0052          1     0.0056    0.00488
    Ford GT Coupe 2006       3435         19    0.00546          1    0.00589    0.00505
Ford F-150 Regular Cab 2012       3435         18    0.00518          1      0.012     0.0103
Ford F-150 Regular Cab 2007       3435         19    0.00547          1    0.00846    0.00733
 Ford Focus Sedan 2007       3435         19    0.00539          1    0.00706    0.00621
Ford E-Series Wagon Van 2012       3435         16    0.00461          1    0.00512    0.00452
Ford Fiesta Sedan 2012       3435         18    0.00514          1    0.00752    0.00675
  GMC Terrain SUV 2012       3435         17    0.00484          1     0.0095     0.0085
   GMC Savana Van 2012       3435         28    0.00796          1      0.016     0.0144
GMC Yukon Hybrid SUV 2012       3435         18    0.00517          1    0.00561     0.0047
   GMC Acadia SUV 2012       3435         19     0.0054          1     0.0072    0.00548
GMC Canyon Extended Cab 2012       3435         16    0.00463          1    0.00961    0.00835
Geo Metro Convertible 1993       3435         19    0.00544          1       0.01    0.00867
HUMMER H3T Crew Cab 2010       3435         17    0.00489          1     0.0069    0.00594
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00541          1      0.011     0.0102
Honda Odyssey Minivan 2012       3435         18    0.00509          1     0.0052    0.00453
Honda Odyssey Minivan 2007       3435         17    0.00484          1     0.0078    0.00687
Honda Accord Coupe 2012       3435         17    0.00486          1      0.005     0.0048
Honda Accord Sedan 2012       3435         17    0.00488          1    0.00964    0.00905
Hyundai Veloster Hatchback 2012       3435         17     0.0049          1    0.00511    0.00399
Hyundai Santa Fe SUV 2012       3435         18    0.00511          1    0.00642    0.00527
Hyundai Tucson SUV 2012       3435         19    0.00547          1    0.00899    0.00805
Hyundai Veracruz SUV 2012       3435         18     0.0051          1    0.00686    0.00604
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00433          1    0.00513    0.00443
Hyundai Elantra Sedan 2007       3435         18    0.00512          1    0.00624    0.00569
Hyundai Accent Sedan 2012       3435         11    0.00316          1    0.00516    0.00435
Hyundai Genesis Sedan 2012       3435         19    0.00548          1    0.00726    0.00621
Hyundai Sonata Sedan 2012       3435         17    0.00485          1    0.00609    0.00485
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00516          1      0.012     0.0101
Hyundai Azera Sedan 2012       3435         18    0.00512          1    0.00625    0.00526
Infiniti G Coupe IPL 2012       3435         15     0.0043          1    0.00441    0.00375
Infiniti QX56 SUV 2011       3435         14    0.00403          1    0.00445    0.00398
Isuzu Ascender SUV 2008       3435         16    0.00454          1     0.0087     0.0078
    Jaguar XK XKR 2012       3435         19    0.00544          1    0.00631    0.00485
 Jeep Patriot SUV 2012       3435         19    0.00545          1    0.00967    0.00836
Jeep Wrangler SUV 2012       3435         18    0.00515          1     0.0124     0.0114
 Jeep Liberty SUV 2012       3435         19    0.00537          1     0.0211     0.0191
Jeep Grand Cherokee SUV 2012       3435         19     0.0054          1    0.00751    0.00639
 Jeep Compass SUV 2012       3435         18    0.00514          1     0.0129     0.0114
Lamborghini Reventon Coupe 2008       3435         15    0.00433          1    0.00455    0.00394
Lamborghini Aventador Coupe 2012       3435         19    0.00541          1    0.00709    0.00557
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00433          1    0.00497    0.00415
Lamborghini Diablo Coupe 2001       3435         19    0.00543          1     0.0103    0.00792
Land Rover Range Rover SUV 2012       3435         18    0.00515          1    0.00669     0.0059
Land Rover LR2 SUV 2012       3435         18    0.00517          1    0.00532    0.00474
Lincoln Town Car Sedan 2011       3435         17     0.0049          1    0.00566    0.00455
MINI Cooper Roadster Convertible 2012       3435         15    0.00432          1     0.0116     0.0103
Maybach Landaulet Convertible 2012       3435         13    0.00371          1    0.00739     0.0061
Mazda Tribute SUV 2011       3435         15    0.00427          1    0.00531    0.00422
McLaren MP4-12C Coupe 2012       3435         19    0.00544          1    0.00577    0.00517
Mercedes-Benz 300-Class Convertible 1993       3435         20    0.00563          1    0.00716    0.00563
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00536          1     0.0059    0.00468
Mercedes-Benz SL-Class Coupe 2009       3435         15    0.00421          1    0.00583    0.00513
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00536          1     0.0168     0.0148
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00544          1    0.00704     0.0062
Mercedes-Benz Sprinter Van 2012       3435         17    0.00479          1    0.00765    0.00678
Mitsubishi Lancer Sedan 2012       3435         20    0.00561          1    0.00597     0.0051
Nissan Leaf Hatchback 2012       3435         18    0.00514          1    0.00608    0.00516
Nissan NV Passenger Van 2012       3435         17    0.00481          1    0.00487    0.00371
Nissan Juke Hatchback 2012       3435         19    0.00544          1    0.00587    0.00504
Nissan 240SX Coupe 1998       3435         19     0.0054          1    0.00736    0.00648
Plymouth Neon Coupe 1999       3435         19     0.0054          1    0.00651    0.00586
Porsche Panamera Sedan 2012       3435         19    0.00546          1    0.00568     0.0049
Ram C-V Cargo Van Minivan 2012       3435         17    0.00492          1     0.0109    0.00968
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00373          1    0.00409    0.00374
Rolls-Royce Ghost Sedan 2012       3435         17    0.00479          1    0.00591     0.0051
Rolls-Royce Phantom Sedan 2012       3435         19    0.00551          1      0.017     0.0137
Scion xD Hatchback 2012       3435         17     0.0049          1    0.00638     0.0058
Spyker C8 Convertible 2009       3435         19    0.00511      0.947    0.00514    0.00408
  Spyker C8 Coupe 2009       3435         18     0.0051          1    0.00557    0.00463
Suzuki Aerio Sedan 2007       3435         16    0.00459          1    0.00629    0.00564
Suzuki Kizashi Sedan 2012       3435         19    0.00534          1     0.0121     0.0107
Suzuki SX4 Hatchback 2012       3435         18    0.00519          1    0.00709    0.00664
 Suzuki SX4 Sedan 2012       3435         17    0.00482          1     0.0188     0.0169
Tesla Model S Sedan 2012       3435         17    0.00489          1     0.0053     0.0045
Toyota Sequoia SUV 2012       3435         17    0.00484          1    0.00652    0.00535
Toyota Camry Sedan 2012       3435         19    0.00548          1    0.00833    0.00726
Toyota Corolla Sedan 2012       3435         19    0.00538          1    0.00577     0.0044
Toyota 4Runner SUV 2012       3435         17    0.00486          1    0.00897    0.00755
Volkswagen Golf Hatchback 2012       3435         18    0.00519          1    0.00763    0.00706
Volkswagen Golf Hatchback 1991       3435         19    0.00544          1    0.00564    0.00516
Volkswagen Beetle Hatchback 2012       3435         18    0.00506          1    0.00936    0.00836
Volvo C30 Hatchback 2012       3435         17    0.00477          1     0.0202     0.0179
  Volvo 240 Sedan 1993       3435         19    0.00539          1    0.00996    0.00913
   Volvo XC90 SUV 2007       3435         18    0.00516          1     0.0122     0.0109
smart fortwo Convertible 2012       3435         16    0.00459          1    0.00883    0.00754
Results saved to yolov5/runs/train/exp8

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 --batch 16

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=16, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:23<00:00, 134.23it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:29<00:00, 117.09it/s]

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp9/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp9
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1       1.1G    0.04281    0.01722     0.1226         29        320: 100% 702/702 [01:42<00:00,  6.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:20<00:00,  5.35it/s]
                   all       3435       3435    0.00487      0.997    0.00813    0.00636

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1       2.1G     0.0225    0.01122     0.1203         32        320: 100% 702/702 [01:34<00:00,  7.40it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:20<00:00,  5.27it/s]
                   all       3435       3435      0.005      0.999    0.00791    0.00687

2 epochs completed in 0.067 hours.
Optimizer stripped from yolov5/runs/train/exp9/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp9/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp9/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 108/108 [00:22<00:00,  4.76it/s]
                   all       3435       3435      0.005      0.999    0.00791    0.00687
AM General Hummer SUV 2000       3435         19    0.00547          1    0.00655    0.00523
   Acura RL Sedan 2012       3435         14    0.00401          1     0.0125      0.011
   Acura TL Sedan 2012       3435         18    0.00519          1    0.00681    0.00608
  Acura TL Type-S 2008       3435         18    0.00508          1    0.00662    0.00592
  Acura TSX Sedan 2012       3435         17    0.00486          1    0.00792    0.00721
Acura Integra Type R 2001       3435         19    0.00512      0.947    0.00535    0.00453
Acura ZDX Hatchback 2012       3435         17    0.00485          1    0.00516    0.00441
Aston Martin V8 Vantage Convertible 2012       3435         19     0.0055          1    0.00736     0.0067
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00481          1    0.00524    0.00416
Aston Martin Virage Convertible 2012       3435         14    0.00399          1     0.0071    0.00647
Aston Martin Virage Coupe 2012       3435         16    0.00458          1    0.00743    0.00654
Audi RS 4 Convertible 2008       3435         15    0.00428          1    0.00529    0.00474
    Audi A5 Coupe 2012       3435         17    0.00489          1    0.00531    0.00449
   Audi TTS Coupe 2012       3435         18    0.00511          1    0.00594    0.00535
    Audi R8 Coupe 2012       3435         18    0.00511          1    0.00834    0.00718
    Audi V8 Sedan 1994       3435         19    0.00536          1      0.007    0.00622
   Audi 100 Sedan 1994       3435         17    0.00484          1     0.0078    0.00695
   Audi 100 Wagon 1994       3435         18    0.00514          1    0.00525    0.00416
Audi TT Hatchback 2011       3435         17     0.0048          1    0.00675    0.00617
    Audi S6 Sedan 2011       3435         19    0.00543          1     0.0159      0.015
Audi S5 Convertible 2012       3435         18     0.0049      0.944    0.00614    0.00564
    Audi S5 Coupe 2012       3435         18    0.00518          1    0.00722    0.00567
    Audi S4 Sedan 2012       3435         17    0.00485          1     0.0062    0.00528
    Audi S4 Sedan 2007       3435         19    0.00538          1    0.00727    0.00675
 Audi TT RS Coupe 2012       3435         17    0.00491          1    0.00546    0.00463
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00424          1     0.0114    0.00932
BMW 1 Series Convertible 2012       3435         15     0.0043          1     0.0091    0.00791
BMW 1 Series Coupe 2012       3435         17    0.00488          1    0.00729    0.00594
BMW 3 Series Sedan 2012       3435         18    0.00482      0.944     0.0352     0.0313
BMW 3 Series Wagon 2012       3435         17    0.00486          1    0.00503    0.00399
BMW 6 Series Convertible 2007       3435         19    0.00536          1    0.00582     0.0052
       BMW X5 SUV 2007       3435         17    0.00489          1     0.0106    0.00915
       BMW X6 SUV 2012       3435         18     0.0051          1     0.0103    0.00971
     BMW M3 Coupe 2012       3435         19    0.00545          1    0.00768    0.00702
     BMW M5 Sedan 2010       3435         17     0.0049          1     0.0082    0.00724
BMW M6 Convertible 2010       3435         17    0.00484          1    0.00627    0.00533
       BMW X3 SUV 2012       3435         17     0.0049          1     0.0118     0.0101
BMW Z4 Convertible 2012       3435         17    0.00492          1    0.00789    0.00757
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00435          1    0.00571    0.00493
Bentley Arnage Sedan 2009       3435         17    0.00491          1    0.00802    0.00714
Bentley Mulsanne Sedan 2011       3435         15    0.00432          1    0.00568     0.0049
Bentley Continental GT Coupe 2012       3435         15    0.00431          1     0.0198     0.0175
Bentley Continental GT Coupe 2007       3435         19    0.00513      0.947    0.00525    0.00458
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00539          1    0.00689    0.00632
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00404          1    0.00737    0.00637
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00533          1    0.00782    0.00652
   Buick Regal GS 2012       3435         14    0.00401          1    0.00518    0.00424
Buick Rainier SUV 2007       3435         18     0.0051          1    0.00817    0.00688
Buick Verano Sedan 2012       3435         16    0.00458          1    0.00632    0.00548
Buick Enclave SUV 2012       3435         18    0.00505          1     0.0132     0.0109
Cadillac CTS-V Sedan 2012       3435         18    0.00516          1    0.00614     0.0052
 Cadillac SRX SUV 2012       3435         17    0.00485          1    0.00632     0.0053
Cadillac Escalade EXT Crew Cab 2007       3435         19    0.00549          1    0.00677    0.00562
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00455          1    0.00733    0.00629
Chevrolet Corvette Convertible 2012       3435         17    0.00487          1    0.00901    0.00832
Chevrolet Corvette ZR1 2012       3435         19    0.00542          1    0.00562    0.00499
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00464          1    0.00532    0.00423
Chevrolet Traverse SUV 2012       3435         19    0.00529          1     0.0152     0.0132
Chevrolet Camaro Convertible 2012       3435         19    0.00541          1    0.00832    0.00749
 Chevrolet HHR SS 2010       3435         15     0.0043          1    0.00527    0.00477
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00611    0.00536
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00459          1    0.00852    0.00727
Chevrolet Sonic Sedan 2012       3435         19    0.00548          1     0.0119    0.00937
Chevrolet Express Cargo Van 2007       3435         13    0.00374          1    0.00909    0.00792
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00544          1    0.00738    0.00634
Chevrolet Cobalt SS 2010       3435         17    0.00488          1    0.00685    0.00542
Chevrolet Malibu Hybrid Sedan 2010       3435         17    0.00491          1    0.00588    0.00504
Chevrolet TrailBlazer SS 2009       3435         16    0.00456          1      0.006    0.00547
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00458          1    0.00634    0.00574
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00521          1      0.014     0.0131
Chevrolet Express Van 2007       3435         14    0.00401          1    0.00415    0.00357
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00534          1    0.00811    0.00682
Chevrolet Malibu Sedan 2007       3435         19    0.00544          1    0.00696     0.0059
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00537          1    0.00624    0.00523
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00546          1     0.0119     0.0107
Chrysler Aspen SUV 2009       3435         19    0.00537          1     0.0108    0.00934
Chrysler Sebring Convertible 2010       3435         17    0.00488          1    0.00527    0.00462
Chrysler Town and Country Minivan 2012       3435         16    0.00452          1    0.00755     0.0064
Chrysler 300 SRT-8 2010       3435         21    0.00604          1    0.00886    0.00784
Chrysler Crossfire Convertible 2008       3435         18    0.00517          1     0.0059    0.00496
Chrysler PT Cruiser Convertible 2008       3435         19    0.00545          1    0.00762    0.00627
Daewoo Nubira Wagon 2002       3435         19    0.00543          1    0.00703    0.00619
Dodge Caliber Wagon 2012       3435         17    0.00492          1    0.00603    0.00469
Dodge Caliber Wagon 2007       3435         18    0.00508          1    0.00587    0.00534
Dodge Caravan Minivan 1997       3435         19    0.00546          1    0.00638    0.00536
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00517          1    0.00676    0.00544
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00549          1    0.00721    0.00644
Dodge Sprinter Cargo Van 2009       3435         17    0.00487          1     0.0058    0.00491
Dodge Journey SUV 2012       3435         19    0.00542          1     0.0121     0.0109
Dodge Dakota Crew Cab 2010       3435         17    0.00485          1    0.00614    0.00515
Dodge Dakota Club Cab 2007       3435         17    0.00486          1    0.00566    0.00466
Dodge Magnum Wagon 2008       3435         16    0.00457          1    0.00581    0.00533
Dodge Challenger SRT8 2011       3435         17    0.00487          1    0.00553    0.00508
Dodge Durango SUV 2012       3435         19    0.00547          1    0.00808    0.00725
Dodge Durango SUV 2007       3435         19    0.00538          1    0.00641    0.00528
Dodge Charger Sedan 2012       3435         17    0.00483          1    0.00829    0.00742
Dodge Charger SRT-8 2009       3435         18    0.00513          1    0.00692    0.00605
Eagle Talon Hatchback 1998       3435         19    0.00545          1    0.00741     0.0068
  FIAT 500 Abarth 2012       3435         12    0.00342          1    0.00416     0.0031
FIAT 500 Convertible 2012       3435         15    0.00429          1    0.00465    0.00434
 Ferrari FF Coupe 2012       3435         18    0.00511          1    0.00738    0.00614
Ferrari California Convertible 2012       3435         17    0.00488          1    0.00741    0.00681
Ferrari 458 Italia Convertible 2012       3435         17    0.00486          1    0.00642    0.00517
Ferrari 458 Italia Coupe 2012       3435         18    0.00512          1    0.00528    0.00479
Fisker Karma Sedan 2012       3435         19     0.0054          1    0.00598    0.00501
Ford F-450 Super Duty Crew Cab 2012       3435         17    0.00481          1    0.00923    0.00794
Ford Mustang Convertible 2007       3435         19    0.00547          1      0.007     0.0056
Ford Freestar Minivan 2007       3435         19    0.00543          1    0.00622    0.00551
Ford Expedition EL SUV 2009       3435         19    0.00542          1    0.00629    0.00573
    Ford Edge SUV 2012       3435         18    0.00511          1     0.0142     0.0119
Ford Ranger SuperCab 2011       3435         18     0.0052          1     0.0056    0.00488
    Ford GT Coupe 2006       3435         19    0.00546          1    0.00589    0.00505
Ford F-150 Regular Cab 2012       3435         18    0.00518          1      0.012     0.0103
Ford F-150 Regular Cab 2007       3435         19    0.00547          1    0.00846    0.00733
 Ford Focus Sedan 2007       3435         19    0.00539          1    0.00706    0.00621
Ford E-Series Wagon Van 2012       3435         16    0.00461          1    0.00512    0.00452
Ford Fiesta Sedan 2012       3435         18    0.00514          1    0.00752    0.00675
  GMC Terrain SUV 2012       3435         17    0.00484          1     0.0095     0.0085
   GMC Savana Van 2012       3435         28    0.00796          1      0.016     0.0144
GMC Yukon Hybrid SUV 2012       3435         18    0.00517          1    0.00561     0.0047
   GMC Acadia SUV 2012       3435         19     0.0054          1     0.0072    0.00548
GMC Canyon Extended Cab 2012       3435         16    0.00463          1    0.00961    0.00835
Geo Metro Convertible 1993       3435         19    0.00544          1       0.01    0.00867
HUMMER H3T Crew Cab 2010       3435         17    0.00489          1     0.0069    0.00594
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00541          1      0.011     0.0102
Honda Odyssey Minivan 2012       3435         18    0.00509          1     0.0052    0.00453
Honda Odyssey Minivan 2007       3435         17    0.00484          1     0.0078    0.00687
Honda Accord Coupe 2012       3435         17    0.00486          1      0.005     0.0048
Honda Accord Sedan 2012       3435         17    0.00488          1    0.00964    0.00905
Hyundai Veloster Hatchback 2012       3435         17     0.0049          1    0.00511    0.00399
Hyundai Santa Fe SUV 2012       3435         18    0.00511          1    0.00642    0.00527
Hyundai Tucson SUV 2012       3435         19    0.00547          1    0.00899    0.00805
Hyundai Veracruz SUV 2012       3435         18     0.0051          1    0.00686    0.00604
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00433          1    0.00513    0.00443
Hyundai Elantra Sedan 2007       3435         18    0.00512          1    0.00624    0.00569
Hyundai Accent Sedan 2012       3435         11    0.00316          1    0.00516    0.00435
Hyundai Genesis Sedan 2012       3435         19    0.00548          1    0.00726    0.00621
Hyundai Sonata Sedan 2012       3435         17    0.00485          1    0.00609    0.00485
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00516          1      0.012     0.0101
Hyundai Azera Sedan 2012       3435         18    0.00512          1    0.00625    0.00526
Infiniti G Coupe IPL 2012       3435         15     0.0043          1    0.00441    0.00375
Infiniti QX56 SUV 2011       3435         14    0.00403          1    0.00445    0.00398
Isuzu Ascender SUV 2008       3435         16    0.00454          1     0.0087     0.0078
    Jaguar XK XKR 2012       3435         19    0.00544          1    0.00631    0.00485
 Jeep Patriot SUV 2012       3435         19    0.00545          1    0.00967    0.00836
Jeep Wrangler SUV 2012       3435         18    0.00515          1     0.0124     0.0114
 Jeep Liberty SUV 2012       3435         19    0.00537          1     0.0211     0.0191
Jeep Grand Cherokee SUV 2012       3435         19     0.0054          1    0.00751    0.00639
 Jeep Compass SUV 2012       3435         18    0.00514          1     0.0129     0.0114
Lamborghini Reventon Coupe 2008       3435         15    0.00433          1    0.00455    0.00394
Lamborghini Aventador Coupe 2012       3435         19    0.00541          1    0.00709    0.00557
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00433          1    0.00497    0.00415
Lamborghini Diablo Coupe 2001       3435         19    0.00543          1     0.0103    0.00792
Land Rover Range Rover SUV 2012       3435         18    0.00515          1    0.00669     0.0059
Land Rover LR2 SUV 2012       3435         18    0.00517          1    0.00532    0.00474
Lincoln Town Car Sedan 2011       3435         17     0.0049          1    0.00566    0.00455
MINI Cooper Roadster Convertible 2012       3435         15    0.00432          1     0.0116     0.0103
Maybach Landaulet Convertible 2012       3435         13    0.00371          1    0.00739     0.0061
Mazda Tribute SUV 2011       3435         15    0.00427          1    0.00531    0.00422
McLaren MP4-12C Coupe 2012       3435         19    0.00544          1    0.00577    0.00517
Mercedes-Benz 300-Class Convertible 1993       3435         20    0.00563          1    0.00716    0.00563
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00536          1     0.0059    0.00468
Mercedes-Benz SL-Class Coupe 2009       3435         15    0.00421          1    0.00583    0.00513
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00536          1     0.0168     0.0148
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00544          1    0.00704     0.0062
Mercedes-Benz Sprinter Van 2012       3435         17    0.00479          1    0.00765    0.00678
Mitsubishi Lancer Sedan 2012       3435         20    0.00561          1    0.00597     0.0051
Nissan Leaf Hatchback 2012       3435         18    0.00514          1    0.00608    0.00516
Nissan NV Passenger Van 2012       3435         17    0.00481          1    0.00487    0.00371
Nissan Juke Hatchback 2012       3435         19    0.00544          1    0.00587    0.00504
Nissan 240SX Coupe 1998       3435         19     0.0054          1    0.00736    0.00648
Plymouth Neon Coupe 1999       3435         19     0.0054          1    0.00651    0.00586
Porsche Panamera Sedan 2012       3435         19    0.00546          1    0.00568     0.0049
Ram C-V Cargo Van Minivan 2012       3435         17    0.00492          1     0.0109    0.00968
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00373          1    0.00409    0.00374
Rolls-Royce Ghost Sedan 2012       3435         17    0.00479          1    0.00591     0.0051
Rolls-Royce Phantom Sedan 2012       3435         19    0.00551          1      0.017     0.0137
Scion xD Hatchback 2012       3435         17     0.0049          1    0.00638     0.0058
Spyker C8 Convertible 2009       3435         19    0.00511      0.947    0.00514    0.00408
  Spyker C8 Coupe 2009       3435         18     0.0051          1    0.00557    0.00463
Suzuki Aerio Sedan 2007       3435         16    0.00459          1    0.00629    0.00564
Suzuki Kizashi Sedan 2012       3435         19    0.00534          1     0.0121     0.0107
Suzuki SX4 Hatchback 2012       3435         18    0.00519          1    0.00709    0.00664
 Suzuki SX4 Sedan 2012       3435         17    0.00482          1     0.0188     0.0169
Tesla Model S Sedan 2012       3435         17    0.00489          1     0.0053     0.0045
Toyota Sequoia SUV 2012       3435         17    0.00484          1    0.00652    0.00535
Toyota Camry Sedan 2012       3435         19    0.00548          1    0.00833    0.00726
Toyota Corolla Sedan 2012       3435         19    0.00538          1    0.00577     0.0044
Toyota 4Runner SUV 2012       3435         17    0.00486          1    0.00897    0.00755
Volkswagen Golf Hatchback 2012       3435         18    0.00519          1    0.00763    0.00706
Volkswagen Golf Hatchback 1991       3435         19    0.00544          1    0.00564    0.00516
Volkswagen Beetle Hatchback 2012       3435         18    0.00506          1    0.00936    0.00836
Volvo C30 Hatchback 2012       3435         17    0.00477          1     0.0202     0.0179
  Volvo 240 Sedan 1993       3435         19    0.00539          1    0.00996    0.00913
   Volvo XC90 SUV 2007       3435         18    0.00516          1     0.0122     0.0109
smart fortwo Convertible 2012       3435         16    0.00459          1    0.00883    0.00754
Results saved to yolov5/runs/train/exp9

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 --batch 32

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=32, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:14<00:00, 149.82it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:28<00:00, 118.81it/s]

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp10/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp10
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1      1.98G    0.04563    0.01772     0.1229         78        320: 100% 351/351 [01:15<00:00,  4.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 54/54 [00:19<00:00,  2.82it/s]
                   all       3435       3435    0.00486      0.998    0.00802    0.00631

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1      3.24G    0.02264    0.01133     0.1204         68        320: 100% 351/351 [01:10<00:00,  4.98it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 54/54 [00:18<00:00,  2.87it/s]
                   all       3435       3435      0.005      0.998    0.00819    0.00694

2 epochs completed in 0.052 hours.
Optimizer stripped from yolov5/runs/train/exp10/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp10/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp10/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 54/54 [00:20<00:00,  2.58it/s]
                   all       3435       3435      0.005      0.998    0.00819    0.00693
AM General Hummer SUV 2000       3435         19    0.00547          1    0.00626     0.0047
   Acura RL Sedan 2012       3435         14    0.00399          1    0.00885    0.00762
   Acura TL Sedan 2012       3435         18     0.0052          1    0.00646    0.00525
  Acura TL Type-S 2008       3435         18    0.00507          1    0.00632     0.0057
  Acura TSX Sedan 2012       3435         17    0.00487          1     0.0135     0.0102
Acura Integra Type R 2001       3435         19    0.00517      0.947     0.0055    0.00455
Acura ZDX Hatchback 2012       3435         17    0.00487          1    0.00501     0.0045
Aston Martin V8 Vantage Convertible 2012       3435         19    0.00549          1    0.00931    0.00818
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00481          1    0.00534    0.00396
Aston Martin Virage Convertible 2012       3435         14    0.00396          1     0.0115    0.00997
Aston Martin Virage Coupe 2012       3435         16    0.00456          1      0.008    0.00696
Audi RS 4 Convertible 2008       3435         15    0.00398      0.933    0.00514    0.00459
    Audi A5 Coupe 2012       3435         17     0.0049          1    0.00529    0.00474
   Audi TTS Coupe 2012       3435         18    0.00511          1    0.00624    0.00537
    Audi R8 Coupe 2012       3435         18    0.00507          1    0.00759    0.00625
    Audi V8 Sedan 1994       3435         19    0.00536          1    0.00674    0.00588
   Audi 100 Sedan 1994       3435         17    0.00482          1    0.00895     0.0074
   Audi 100 Wagon 1994       3435         18    0.00513          1    0.00545    0.00422
Audi TT Hatchback 2011       3435         17    0.00478          1    0.00622    0.00509
    Audi S6 Sedan 2011       3435         19    0.00542          1     0.0125     0.0107
Audi S5 Convertible 2012       3435         18    0.00492      0.944    0.00608    0.00543
    Audi S5 Coupe 2012       3435         18    0.00518          1    0.00568    0.00448
    Audi S4 Sedan 2012       3435         17    0.00483          1    0.00542    0.00474
    Audi S4 Sedan 2007       3435         19    0.00538          1    0.00916    0.00786
 Audi TT RS Coupe 2012       3435         17    0.00491          1    0.00548    0.00454
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00426          1     0.0123     0.0106
BMW 1 Series Convertible 2012       3435         15     0.0043          1    0.00709    0.00627
BMW 1 Series Coupe 2012       3435         17    0.00489          1     0.0106    0.00823
BMW 3 Series Sedan 2012       3435         18    0.00482      0.944     0.0651     0.0583
BMW 3 Series Wagon 2012       3435         17    0.00485          1    0.00767    0.00448
BMW 6 Series Convertible 2007       3435         19    0.00536          1    0.00602    0.00532
       BMW X5 SUV 2007       3435         17    0.00486          1    0.00755    0.00635
       BMW X6 SUV 2012       3435         18    0.00509          1     0.0105    0.00965
     BMW M3 Coupe 2012       3435         19    0.00543          1    0.00824    0.00672
     BMW M5 Sedan 2010       3435         17    0.00489          1    0.00827    0.00719
BMW M6 Convertible 2010       3435         17    0.00485          1    0.00526    0.00449
       BMW X3 SUV 2012       3435         17    0.00491          1    0.00856    0.00724
BMW Z4 Convertible 2012       3435         17    0.00492          1    0.00733    0.00632
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00436          1    0.00711    0.00616
Bentley Arnage Sedan 2009       3435         17    0.00493          1    0.00811    0.00682
Bentley Mulsanne Sedan 2011       3435         15     0.0043          1     0.0057    0.00483
Bentley Continental GT Coupe 2012       3435         15    0.00433          1     0.0141     0.0127
Bentley Continental GT Coupe 2007       3435         19    0.00513      0.947    0.00517    0.00426
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00538          1    0.00723    0.00637
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00404          1    0.00674    0.00571
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00536          1    0.00863    0.00725
   Buick Regal GS 2012       3435         14    0.00401          1    0.00505    0.00455
Buick Rainier SUV 2007       3435         18    0.00511          1    0.00912    0.00753
Buick Verano Sedan 2012       3435         16    0.00457          1    0.00572    0.00493
Buick Enclave SUV 2012       3435         18    0.00506          1    0.00783    0.00667
Cadillac CTS-V Sedan 2012       3435         18    0.00518          1    0.00659    0.00553
 Cadillac SRX SUV 2012       3435         17    0.00485          1    0.00633    0.00543
Cadillac Escalade EXT Crew Cab 2007       3435         19     0.0055          1    0.00775    0.00621
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00457          1    0.00728    0.00652
Chevrolet Corvette Convertible 2012       3435         17    0.00488          1    0.00733    0.00645
Chevrolet Corvette ZR1 2012       3435         19    0.00538          1    0.00564    0.00465
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00465          1    0.00538    0.00431
Chevrolet Traverse SUV 2012       3435         19    0.00529          1    0.00663    0.00542
Chevrolet Camaro Convertible 2012       3435         19    0.00538          1    0.00909    0.00785
 Chevrolet HHR SS 2010       3435         15     0.0043          1    0.00541    0.00462
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00586    0.00487
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00458          1    0.00551    0.00461
Chevrolet Sonic Sedan 2012       3435         19     0.0055          1     0.0166     0.0122
Chevrolet Express Cargo Van 2007       3435         13    0.00374          1    0.00903    0.00816
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00542          1    0.00747    0.00647
Chevrolet Cobalt SS 2010       3435         17    0.00489          1    0.00734     0.0054
Chevrolet Malibu Hybrid Sedan 2010       3435         17     0.0049          1    0.00641    0.00563
Chevrolet TrailBlazer SS 2009       3435         16    0.00456          1    0.00502    0.00448
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00455          1    0.00677    0.00593
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00519          1      0.012     0.0106
Chevrolet Express Van 2007       3435         14      0.004          1    0.00404    0.00362
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00535          1    0.00869    0.00761
Chevrolet Malibu Sedan 2007       3435         19    0.00545          1    0.00643     0.0056
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00536          1    0.00632    0.00543
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00548          1     0.0162     0.0137
Chrysler Aspen SUV 2009       3435         19    0.00539          1     0.0136     0.0113
Chrysler Sebring Convertible 2010       3435         17    0.00486          1    0.00501    0.00429
Chrysler Town and Country Minivan 2012       3435         16    0.00452          1    0.00519    0.00428
Chrysler 300 SRT-8 2010       3435         21    0.00603          1     0.0105    0.00868
Chrysler Crossfire Convertible 2008       3435         18    0.00517          1    0.00644    0.00569
Chrysler PT Cruiser Convertible 2008       3435         19    0.00547          1    0.00699    0.00559
Daewoo Nubira Wagon 2002       3435         19    0.00547          1    0.00791    0.00638
Dodge Caliber Wagon 2012       3435         17    0.00491          1    0.00554    0.00461
Dodge Caliber Wagon 2007       3435         18    0.00508          1    0.00606    0.00518
Dodge Caravan Minivan 1997       3435         19    0.00544          1    0.00612    0.00512
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00517          1    0.00698    0.00582
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00548          1    0.00882    0.00777
Dodge Sprinter Cargo Van 2009       3435         17    0.00485          1    0.00681     0.0055
Dodge Journey SUV 2012       3435         19    0.00542          1    0.00692    0.00585
Dodge Dakota Crew Cab 2010       3435         17    0.00484          1    0.00729    0.00606
Dodge Dakota Club Cab 2007       3435         17    0.00485          1    0.00576    0.00475
Dodge Magnum Wagon 2008       3435         16    0.00457          1    0.00608    0.00515
Dodge Challenger SRT8 2011       3435         17    0.00488          1    0.00551    0.00488
Dodge Durango SUV 2012       3435         19    0.00546          1    0.00755    0.00667
Dodge Durango SUV 2007       3435         19    0.00536          1    0.00657    0.00521
Dodge Charger Sedan 2012       3435         17    0.00484          1    0.00997    0.00862
Dodge Charger SRT-8 2009       3435         18    0.00512          1    0.00688    0.00589
Eagle Talon Hatchback 1998       3435         19    0.00546          1    0.00747    0.00656
  FIAT 500 Abarth 2012       3435         12    0.00342          1    0.00347    0.00277
FIAT 500 Convertible 2012       3435         15    0.00427          1    0.00472    0.00424
 Ferrari FF Coupe 2012       3435         18    0.00513          1    0.00852     0.0065
Ferrari California Convertible 2012       3435         17     0.0049          1    0.00996    0.00833
Ferrari 458 Italia Convertible 2012       3435         17    0.00485          1    0.00548    0.00439
Ferrari 458 Italia Coupe 2012       3435         18    0.00513          1    0.00532    0.00447
Fisker Karma Sedan 2012       3435         19    0.00538          1    0.00657     0.0054
Ford F-450 Super Duty Crew Cab 2012       3435         17     0.0048          1    0.00861    0.00796
Ford Mustang Convertible 2007       3435         19    0.00546          1    0.00757      0.006
Ford Freestar Minivan 2007       3435         19    0.00544          1    0.00579    0.00524
Ford Expedition EL SUV 2009       3435         19    0.00544          1    0.00668    0.00586
    Ford Edge SUV 2012       3435         18    0.00512          1     0.0115     0.0094
Ford Ranger SuperCab 2011       3435         18    0.00519          1    0.00541    0.00501
    Ford GT Coupe 2006       3435         19    0.00544          1    0.00598    0.00496
Ford F-150 Regular Cab 2012       3435         18    0.00518          1     0.0117     0.0105
Ford F-150 Regular Cab 2007       3435         19    0.00546          1    0.00965     0.0083
 Ford Focus Sedan 2007       3435         19    0.00538          1    0.00709    0.00583
Ford E-Series Wagon Van 2012       3435         16    0.00462          1     0.0055    0.00478
Ford Fiesta Sedan 2012       3435         18    0.00514          1    0.00613    0.00534
  GMC Terrain SUV 2012       3435         17    0.00484          1    0.00932    0.00807
   GMC Savana Van 2012       3435         28      0.008          1     0.0153     0.0135
GMC Yukon Hybrid SUV 2012       3435         18    0.00518          1    0.00686    0.00581
   GMC Acadia SUV 2012       3435         19    0.00537          1    0.00714    0.00517
GMC Canyon Extended Cab 2012       3435         16    0.00462          1    0.00743     0.0065
Geo Metro Convertible 1993       3435         19    0.00543          1    0.00949    0.00802
HUMMER H3T Crew Cab 2010       3435         17    0.00488          1    0.00883    0.00718
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00541          1     0.0188      0.017
Honda Odyssey Minivan 2012       3435         18    0.00509          1    0.00521    0.00445
Honda Odyssey Minivan 2007       3435         17    0.00483          1    0.00822    0.00716
Honda Accord Coupe 2012       3435         17    0.00484          1    0.00533    0.00485
Honda Accord Sedan 2012       3435         17    0.00488          1    0.00917    0.00875
Hyundai Veloster Hatchback 2012       3435         17     0.0049          1     0.0052    0.00406
Hyundai Santa Fe SUV 2012       3435         18    0.00509          1    0.00606    0.00529
Hyundai Tucson SUV 2012       3435         19    0.00547          1    0.00867    0.00739
Hyundai Veracruz SUV 2012       3435         18     0.0051          1     0.0063    0.00566
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00434          1    0.00497    0.00428
Hyundai Elantra Sedan 2007       3435         18    0.00513          1    0.00623     0.0058
Hyundai Accent Sedan 2012       3435         11    0.00315          1    0.00573    0.00504
Hyundai Genesis Sedan 2012       3435         19    0.00548          1     0.0086    0.00712
Hyundai Sonata Sedan 2012       3435         17    0.00484          1    0.00546    0.00432
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00515          1    0.00936    0.00766
Hyundai Azera Sedan 2012       3435         18    0.00512          1    0.00668    0.00549
Infiniti G Coupe IPL 2012       3435         15    0.00432          1    0.00449    0.00366
Infiniti QX56 SUV 2011       3435         14    0.00403          1    0.00477    0.00409
Isuzu Ascender SUV 2008       3435         16    0.00454          1    0.00899     0.0077
    Jaguar XK XKR 2012       3435         19    0.00546          1      0.008    0.00598
 Jeep Patriot SUV 2012       3435         19    0.00545          1     0.0163     0.0141
Jeep Wrangler SUV 2012       3435         18    0.00514          1    0.00788    0.00697
 Jeep Liberty SUV 2012       3435         19    0.00537          1     0.0191     0.0174
Jeep Grand Cherokee SUV 2012       3435         19    0.00539          1    0.00805    0.00663
 Jeep Compass SUV 2012       3435         18    0.00513          1    0.00985    0.00881
Lamborghini Reventon Coupe 2008       3435         15     0.0043          1    0.00465    0.00415
Lamborghini Aventador Coupe 2012       3435         19    0.00541          1    0.00765    0.00538
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00432          1    0.00531    0.00454
Lamborghini Diablo Coupe 2001       3435         19    0.00542          1     0.0103    0.00832
Land Rover Range Rover SUV 2012       3435         18    0.00514          1    0.00662    0.00609
Land Rover LR2 SUV 2012       3435         18    0.00517          1    0.00563     0.0052
Lincoln Town Car Sedan 2011       3435         17    0.00489          1    0.00505    0.00386
MINI Cooper Roadster Convertible 2012       3435         15    0.00432          1     0.0169     0.0129
Maybach Landaulet Convertible 2012       3435         13    0.00371          1     0.0052    0.00426
Mazda Tribute SUV 2011       3435         15    0.00426          1    0.00504    0.00412
McLaren MP4-12C Coupe 2012       3435         19    0.00546          1    0.00561    0.00495
Mercedes-Benz 300-Class Convertible 1993       3435         20    0.00564          1    0.00603    0.00474
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00537          1    0.00564    0.00457
Mercedes-Benz SL-Class Coupe 2009       3435         15    0.00422          1    0.00663    0.00571
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00536          1     0.0142     0.0121
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00543          1    0.00624     0.0054
Mercedes-Benz Sprinter Van 2012       3435         17    0.00477          1    0.00593    0.00481
Mitsubishi Lancer Sedan 2012       3435         20    0.00562          1    0.00774    0.00672
Nissan Leaf Hatchback 2012       3435         18    0.00514          1    0.00796    0.00672
Nissan NV Passenger Van 2012       3435         17    0.00479          1     0.0063    0.00494
Nissan Juke Hatchback 2012       3435         19    0.00543          1    0.00588    0.00478
Nissan 240SX Coupe 1998       3435         19     0.0054          1    0.00706     0.0061
Plymouth Neon Coupe 1999       3435         19     0.0054          1     0.0101    0.00946
Porsche Panamera Sedan 2012       3435         19    0.00547          1    0.00576     0.0051
Ram C-V Cargo Van Minivan 2012       3435         17    0.00494          1    0.00759    0.00656
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00371          1    0.00439    0.00368
Rolls-Royce Ghost Sedan 2012       3435         17    0.00479          1     0.0059    0.00501
Rolls-Royce Phantom Sedan 2012       3435         19    0.00551          1     0.0203     0.0149
Scion xD Hatchback 2012       3435         17     0.0049          1    0.00731    0.00634
Spyker C8 Convertible 2009       3435         19     0.0051      0.947     0.0053    0.00409
  Spyker C8 Coupe 2009       3435         18    0.00512          1      0.011    0.00897
Suzuki Aerio Sedan 2007       3435         16    0.00459          1    0.00635    0.00559
Suzuki Kizashi Sedan 2012       3435         19    0.00533          1    0.00801    0.00655
Suzuki SX4 Hatchback 2012       3435         18    0.00518          1    0.00599    0.00538
 Suzuki SX4 Sedan 2012       3435         17    0.00481          1     0.0328     0.0293
Tesla Model S Sedan 2012       3435         17    0.00487          1    0.00503    0.00391
Toyota Sequoia SUV 2012       3435         17    0.00482          1    0.00727    0.00612
Toyota Camry Sedan 2012       3435         19    0.00546          1    0.00974    0.00795
Toyota Corolla Sedan 2012       3435         19    0.00538          1    0.00579    0.00441
Toyota 4Runner SUV 2012       3435         17    0.00485          1     0.0158     0.0138
Volkswagen Golf Hatchback 2012       3435         18    0.00519          1    0.00799    0.00716
Volkswagen Golf Hatchback 1991       3435         19    0.00543          1    0.00571     0.0051
Volkswagen Beetle Hatchback 2012       3435         18    0.00507          1    0.00977     0.0079
Volvo C30 Hatchback 2012       3435         17    0.00477          1     0.0172      0.012
  Volvo 240 Sedan 1993       3435         19    0.00537          1    0.00945    0.00813
   Volvo XC90 SUV 2007       3435         18    0.00513          1     0.0131     0.0113
smart fortwo Convertible 2012       3435         16     0.0046          1    0.00832    0.00733
Results saved to yolov5/runs/train/exp10

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 --batch 64

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=64, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:18<00:00, 142.57it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:29<00:00, 115.74it/s]

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp11/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp11
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1      3.74G    0.05197    0.01909     0.1234         88        320: 100% 176/176 [01:03<00:00,  2.79it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435    0.00359      0.991    0.00732    0.00467

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1      6.84G    0.02558    0.01286      0.121         74        320: 100% 176/176 [00:59<00:00,  2.96it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.44it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00679

2 epochs completed in 0.046 hours.
Optimizer stripped from yolov5/runs/train/exp11/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp11/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp11/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00678
AM General Hummer SUV 2000       3435         19    0.00546          1    0.00688    0.00521
   Acura RL Sedan 2012       3435         14    0.00398          1     0.0124    0.00992
   Acura TL Sedan 2012       3435         18    0.00517          1    0.00696    0.00549
  Acura TL Type-S 2008       3435         18    0.00493          1    0.00588    0.00475
  Acura TSX Sedan 2012       3435         17    0.00477          1    0.00643    0.00516
Acura Integra Type R 2001       3435         19    0.00516      0.947    0.00672    0.00501
Acura ZDX Hatchback 2012       3435         17    0.00477          1    0.00629    0.00475
Aston Martin V8 Vantage Convertible 2012       3435         19    0.00546          1    0.00899    0.00754
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00473          1    0.00551    0.00401
Aston Martin Virage Convertible 2012       3435         14    0.00394          1      0.018      0.015
Aston Martin Virage Coupe 2012       3435         16     0.0045          1    0.00875    0.00673
Audi RS 4 Convertible 2008       3435         15    0.00417          1    0.00745    0.00645
    Audi A5 Coupe 2012       3435         17    0.00483          1      0.005    0.00389
   Audi TTS Coupe 2012       3435         18    0.00501          1    0.00644    0.00555
    Audi R8 Coupe 2012       3435         18    0.00503          1    0.00667    0.00538
    Audi V8 Sedan 1994       3435         19    0.00526          1    0.00776    0.00591
   Audi 100 Sedan 1994       3435         17    0.00474          1    0.00947    0.00833
   Audi 100 Wagon 1994       3435         18    0.00514          1    0.00552    0.00327
Audi TT Hatchback 2011       3435         17    0.00465          1    0.00651    0.00546
    Audi S6 Sedan 2011       3435         19    0.00535          1    0.00631    0.00544
Audi S5 Convertible 2012       3435         18    0.00491      0.944    0.00688    0.00608
    Audi S5 Coupe 2012       3435         18    0.00517          1    0.00603     0.0045
    Audi S4 Sedan 2012       3435         17    0.00473          1    0.00541    0.00399
    Audi S4 Sedan 2007       3435         19    0.00532          1     0.0066    0.00581
 Audi TT RS Coupe 2012       3435         17    0.00489          1    0.00498    0.00367
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00424          1     0.0218     0.0137
BMW 1 Series Convertible 2012       3435         15    0.00426          1    0.00704    0.00569
BMW 1 Series Coupe 2012       3435         17    0.00485          1     0.0228     0.0192
BMW 3 Series Sedan 2012       3435         18    0.00477      0.944     0.0082    0.00672
BMW 3 Series Wagon 2012       3435         17    0.00475          1     0.0063     0.0041
BMW 6 Series Convertible 2007       3435         19    0.00521          1     0.0061    0.00512
       BMW X5 SUV 2007       3435         17     0.0048          1    0.00686     0.0056
       BMW X6 SUV 2012       3435         18    0.00493          1     0.0191     0.0172
     BMW M3 Coupe 2012       3435         19    0.00541          1    0.00711    0.00619
     BMW M5 Sedan 2010       3435         17    0.00487          1    0.00979    0.00802
BMW M6 Convertible 2010       3435         17    0.00478          1    0.00653     0.0048
       BMW X3 SUV 2012       3435         17    0.00483          1    0.00877    0.00725
BMW Z4 Convertible 2012       3435         17    0.00489          1     0.0104    0.00932
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00436          1    0.00449    0.00347
Bentley Arnage Sedan 2009       3435         17    0.00485          1    0.00734    0.00592
Bentley Mulsanne Sedan 2011       3435         15    0.00424          1    0.00567    0.00458
Bentley Continental GT Coupe 2012       3435         15     0.0043          1     0.0151     0.0123
Bentley Continental GT Coupe 2007       3435         19    0.00499      0.947    0.00551    0.00443
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00529          1    0.00821    0.00695
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00395          1     0.0118    0.00915
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00531          1     0.0101    0.00745
   Buick Regal GS 2012       3435         14    0.00395          1    0.00473    0.00362
Buick Rainier SUV 2007       3435         18      0.005          1     0.0221     0.0152
Buick Verano Sedan 2012       3435         16     0.0045          1    0.00545    0.00441
Buick Enclave SUV 2012       3435         18    0.00489          1    0.00834    0.00683
Cadillac CTS-V Sedan 2012       3435         18    0.00515          1    0.00583    0.00411
 Cadillac SRX SUV 2012       3435         17     0.0048          1    0.00675    0.00548
Cadillac Escalade EXT Crew Cab 2007       3435         19     0.0055          1    0.00711    0.00549
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00452          1    0.00718    0.00654
Chevrolet Corvette Convertible 2012       3435         17    0.00488          1    0.00929    0.00744
Chevrolet Corvette ZR1 2012       3435         19    0.00528          1     0.0063    0.00535
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00464          1    0.00489    0.00361
Chevrolet Traverse SUV 2012       3435         19    0.00517          1     0.0071    0.00511
Chevrolet Camaro Convertible 2012       3435         19    0.00536          1    0.00792    0.00682
 Chevrolet HHR SS 2010       3435         15    0.00429          1    0.00475    0.00392
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00611    0.00499
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00451          1     0.0068    0.00527
Chevrolet Sonic Sedan 2012       3435         19    0.00543          1     0.0115    0.00868
Chevrolet Express Cargo Van 2007       3435         13     0.0037          1    0.00768     0.0066
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00534          1    0.00922     0.0079
Chevrolet Cobalt SS 2010       3435         17    0.00488          1    0.00595    0.00443
Chevrolet Malibu Hybrid Sedan 2010       3435         17    0.00489          1    0.00606     0.0049
Chevrolet TrailBlazer SS 2009       3435         16    0.00447          1    0.00473      0.004
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00451          1    0.00534    0.00436
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00516          1     0.0134     0.0116
Chevrolet Express Van 2007       3435         14    0.00394          1    0.00399    0.00346
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00519          1    0.00792    0.00603
Chevrolet Malibu Sedan 2007       3435         19     0.0054          1     0.0091    0.00756
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00527          1    0.00679    0.00509
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00546          1    0.00926    0.00715
Chrysler Aspen SUV 2009       3435         19    0.00532          1     0.0112    0.00949
Chrysler Sebring Convertible 2010       3435         17    0.00486          1    0.00921    0.00783
Chrysler Town and Country Minivan 2012       3435         16    0.00444          1    0.00471    0.00405
Chrysler 300 SRT-8 2010       3435         21    0.00596          1    0.00884    0.00716
Chrysler Crossfire Convertible 2008       3435         18    0.00512          1     0.0069    0.00532
Chrysler PT Cruiser Convertible 2008       3435         19    0.00543          1    0.00702    0.00547
Daewoo Nubira Wagon 2002       3435         19    0.00543          1     0.0072    0.00628
Dodge Caliber Wagon 2012       3435         17     0.0049          1    0.00651    0.00428
Dodge Caliber Wagon 2007       3435         18    0.00498          1    0.00585    0.00496
Dodge Caravan Minivan 1997       3435         19    0.00535          1    0.00634    0.00482
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00508          1    0.00642      0.005
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00546          1    0.00731    0.00572
Dodge Sprinter Cargo Van 2009       3435         17    0.00482          1    0.00529    0.00412
Dodge Journey SUV 2012       3435         19     0.0054          1      0.061     0.0545
Dodge Dakota Crew Cab 2010       3435         17    0.00476          1    0.00669    0.00508
Dodge Dakota Club Cab 2007       3435         17    0.00482          1     0.0061     0.0046
Dodge Magnum Wagon 2008       3435         16    0.00452          1    0.00735     0.0064
Dodge Challenger SRT8 2011       3435         17    0.00483          1    0.00529    0.00469
Dodge Durango SUV 2012       3435         19    0.00543          1    0.00789    0.00707
Dodge Durango SUV 2007       3435         19    0.00531          1    0.00639    0.00498
Dodge Charger Sedan 2012       3435         17     0.0048          1    0.00731    0.00608
Dodge Charger SRT-8 2009       3435         18    0.00503          1      0.007    0.00602
Eagle Talon Hatchback 1998       3435         19    0.00541          1     0.0136     0.0127
  FIAT 500 Abarth 2012       3435         12    0.00337          1    0.00442    0.00318
FIAT 500 Convertible 2012       3435         15     0.0042          1    0.00675    0.00603
 Ferrari FF Coupe 2012       3435         18    0.00506          1      0.011    0.00865
Ferrari California Convertible 2012       3435         17    0.00487          1    0.00694    0.00596
Ferrari 458 Italia Convertible 2012       3435         17    0.00479          1    0.00558    0.00399
Ferrari 458 Italia Coupe 2012       3435         18    0.00505          1    0.00581    0.00491
Fisker Karma Sedan 2012       3435         19    0.00529          1     0.0071    0.00526
Ford F-450 Super Duty Crew Cab 2012       3435         17    0.00476          1      0.012    0.00969
Ford Mustang Convertible 2007       3435         19    0.00542          1    0.00601    0.00448
Ford Freestar Minivan 2007       3435         19    0.00536          1    0.00579    0.00493
Ford Expedition EL SUV 2009       3435         19    0.00543          1    0.00579    0.00468
    Ford Edge SUV 2012       3435         18    0.00507          1    0.00944    0.00764
Ford Ranger SuperCab 2011       3435         18    0.00511          1    0.00525    0.00446
    Ford GT Coupe 2006       3435         19    0.00538          1    0.00631    0.00487
Ford F-150 Regular Cab 2012       3435         18    0.00515          1    0.00899    0.00698
Ford F-150 Regular Cab 2007       3435         19    0.00539          1     0.0087    0.00732
 Ford Focus Sedan 2007       3435         19     0.0053          1     0.0073    0.00632
Ford E-Series Wagon Van 2012       3435         16    0.00464          1    0.00543    0.00449
Ford Fiesta Sedan 2012       3435         18    0.00508          1    0.00713    0.00608
  GMC Terrain SUV 2012       3435         17    0.00474          1    0.00754    0.00638
   GMC Savana Van 2012       3435         28    0.00798          1     0.0128      0.011
GMC Yukon Hybrid SUV 2012       3435         18    0.00517          1    0.00727    0.00535
   GMC Acadia SUV 2012       3435         19    0.00524          1    0.00707    0.00424
GMC Canyon Extended Cab 2012       3435         16     0.0046          1    0.00572    0.00426
Geo Metro Convertible 1993       3435         19     0.0054          1    0.00694    0.00542
HUMMER H3T Crew Cab 2010       3435         17    0.00485          1    0.00756    0.00626
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00532          1     0.0064    0.00556
Honda Odyssey Minivan 2012       3435         18    0.00496          1    0.00524    0.00435
Honda Odyssey Minivan 2007       3435         17    0.00475          1    0.00596    0.00519
Honda Accord Coupe 2012       3435         17    0.00474          1    0.00525    0.00428
Honda Accord Sedan 2012       3435         17    0.00489          1    0.00761    0.00675
Hyundai Veloster Hatchback 2012       3435         17    0.00487          1    0.00572     0.0046
Hyundai Santa Fe SUV 2012       3435         18    0.00502          1    0.00759    0.00574
Hyundai Tucson SUV 2012       3435         19    0.00544          1    0.00969    0.00843
Hyundai Veracruz SUV 2012       3435         18    0.00498          1     0.0082    0.00701
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00434          1    0.00458    0.00382
Hyundai Elantra Sedan 2007       3435         18    0.00504          1    0.00823    0.00669
Hyundai Accent Sedan 2012       3435         11    0.00311          1    0.00559    0.00404
Hyundai Genesis Sedan 2012       3435         19    0.00544          1    0.00686    0.00577
Hyundai Sonata Sedan 2012       3435         17    0.00478          1    0.00579    0.00463
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00509          1    0.00898    0.00664
Hyundai Azera Sedan 2012       3435         18    0.00501          1    0.00636    0.00465
Infiniti G Coupe IPL 2012       3435         15     0.0043          1     0.0055    0.00389
Infiniti QX56 SUV 2011       3435         14    0.00397          1    0.00509    0.00462
Isuzu Ascender SUV 2008       3435         16    0.00446          1    0.00784    0.00668
    Jaguar XK XKR 2012       3435         19    0.00543          1     0.0134       0.01
 Jeep Patriot SUV 2012       3435         19     0.0054          1      0.017     0.0137
Jeep Wrangler SUV 2012       3435         18    0.00508          1    0.00894    0.00732
 Jeep Liberty SUV 2012       3435         19    0.00522          1     0.0208     0.0188
Jeep Grand Cherokee SUV 2012       3435         19    0.00529          1    0.00809    0.00636
 Jeep Compass SUV 2012       3435         18    0.00504          1    0.00924    0.00785
Lamborghini Reventon Coupe 2008       3435         15    0.00427          1    0.00499    0.00433
Lamborghini Aventador Coupe 2012       3435         19    0.00536          1    0.00666    0.00485
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00429          1     0.0055    0.00407
Lamborghini Diablo Coupe 2001       3435         19    0.00539          1     0.0165     0.0119
Land Rover Range Rover SUV 2012       3435         18    0.00502          1    0.00749    0.00635
Land Rover LR2 SUV 2012       3435         18    0.00513          1    0.00572    0.00469
Lincoln Town Car Sedan 2011       3435         17    0.00486          1    0.00528    0.00425
MINI Cooper Roadster Convertible 2012       3435         15    0.00427          1     0.0145     0.0122
Maybach Landaulet Convertible 2012       3435         13    0.00367          1    0.00431    0.00338
Mazda Tribute SUV 2011       3435         15    0.00419          1     0.0047    0.00354
McLaren MP4-12C Coupe 2012       3435         19    0.00545          1    0.00577    0.00506
Mercedes-Benz 300-Class Convertible 1993       3435         20     0.0055          1    0.00692    0.00526
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00524          1    0.00571     0.0042
Mercedes-Benz SL-Class Coupe 2009       3435         15     0.0041          1    0.00664    0.00541
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00519          1     0.0101    0.00846
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00539          1    0.00744    0.00622
Mercedes-Benz Sprinter Van 2012       3435         17    0.00466          1    0.00709    0.00617
Mitsubishi Lancer Sedan 2012       3435         20    0.00554          1    0.00582    0.00459
Nissan Leaf Hatchback 2012       3435         18    0.00509          1    0.00668    0.00554
Nissan NV Passenger Van 2012       3435         17    0.00469          1    0.00607    0.00479
Nissan Juke Hatchback 2012       3435         19    0.00537          1    0.00562     0.0046
Nissan 240SX Coupe 1998       3435         19    0.00536          1    0.00627    0.00499
Plymouth Neon Coupe 1999       3435         19    0.00531          1    0.00652    0.00553
Porsche Panamera Sedan 2012       3435         19    0.00545          1     0.0056     0.0046
Ram C-V Cargo Van Minivan 2012       3435         17    0.00493          1     0.0201     0.0162
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00366          1     0.0428     0.0379
Rolls-Royce Ghost Sedan 2012       3435         17    0.00468          1    0.00506    0.00388
Rolls-Royce Phantom Sedan 2012       3435         19     0.0055          1     0.0142     0.0107
Scion xD Hatchback 2012       3435         17     0.0049          1     0.0057    0.00493
Spyker C8 Convertible 2009       3435         19      0.005      0.947    0.00522    0.00429
  Spyker C8 Coupe 2009       3435         18    0.00506          1    0.00576    0.00448
Suzuki Aerio Sedan 2007       3435         16    0.00458          1    0.00593    0.00492
Suzuki Kizashi Sedan 2012       3435         19    0.00522          1    0.00949     0.0078
Suzuki SX4 Hatchback 2012       3435         18    0.00516          1    0.00677    0.00604
 Suzuki SX4 Sedan 2012       3435         17    0.00471          1     0.0127     0.0108
Tesla Model S Sedan 2012       3435         17    0.00482          1    0.00497    0.00384
Toyota Sequoia SUV 2012       3435         17    0.00471          1     0.0107    0.00856
Toyota Camry Sedan 2012       3435         19    0.00548          1    0.00743    0.00533
Toyota Corolla Sedan 2012       3435         19     0.0053          1    0.00581    0.00423
Toyota 4Runner SUV 2012       3435         17    0.00477          1     0.0149     0.0124
Volkswagen Golf Hatchback 2012       3435         18     0.0052          1    0.00831    0.00731
Volkswagen Golf Hatchback 1991       3435         19    0.00533          1    0.00551    0.00464
Volkswagen Beetle Hatchback 2012       3435         18    0.00498          1    0.00943     0.0085
Volvo C30 Hatchback 2012       3435         17    0.00465          1    0.00987    0.00812
  Volvo 240 Sedan 1993       3435         19    0.00527          1    0.00961    0.00847
   Volvo XC90 SUV 2007       3435         18    0.00504          1    0.00963    0.00857
smart fortwo Convertible 2012       3435         16    0.00455          1    0.00695    0.00578
Results saved to yolov5/runs/train/exp11

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 --batch 64 --workers 16

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=64, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=16, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:21<00:00, 137.31it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:35<00:00, 97.56it/s] 

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp12/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp12
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1      3.74G    0.05197    0.01909     0.1234         88        320: 100% 176/176 [01:03<00:00,  2.76it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.36it/s]
                   all       3435       3435    0.00359      0.991    0.00732    0.00467

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1      6.84G    0.02558    0.01286      0.121         74        320: 100% 176/176 [01:00<00:00,  2.91it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.45it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00679

2 epochs completed in 0.046 hours.
Optimizer stripped from yolov5/runs/train/exp12/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp12/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp12/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.27it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00678
AM General Hummer SUV 2000       3435         19    0.00546          1    0.00688    0.00521
   Acura RL Sedan 2012       3435         14    0.00398          1     0.0124    0.00992
   Acura TL Sedan 2012       3435         18    0.00517          1    0.00696    0.00549
  Acura TL Type-S 2008       3435         18    0.00493          1    0.00588    0.00475
  Acura TSX Sedan 2012       3435         17    0.00477          1    0.00643    0.00516
Acura Integra Type R 2001       3435         19    0.00516      0.947    0.00672    0.00501
Acura ZDX Hatchback 2012       3435         17    0.00477          1    0.00629    0.00475
Aston Martin V8 Vantage Convertible 2012       3435         19    0.00546          1    0.00899    0.00754
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00473          1    0.00551    0.00401
Aston Martin Virage Convertible 2012       3435         14    0.00394          1      0.018      0.015
Aston Martin Virage Coupe 2012       3435         16     0.0045          1    0.00875    0.00673
Audi RS 4 Convertible 2008       3435         15    0.00417          1    0.00745    0.00645
    Audi A5 Coupe 2012       3435         17    0.00483          1      0.005    0.00389
   Audi TTS Coupe 2012       3435         18    0.00501          1    0.00644    0.00555
    Audi R8 Coupe 2012       3435         18    0.00503          1    0.00667    0.00538
    Audi V8 Sedan 1994       3435         19    0.00526          1    0.00776    0.00591
   Audi 100 Sedan 1994       3435         17    0.00474          1    0.00947    0.00833
   Audi 100 Wagon 1994       3435         18    0.00514          1    0.00552    0.00327
Audi TT Hatchback 2011       3435         17    0.00465          1    0.00651    0.00546
    Audi S6 Sedan 2011       3435         19    0.00535          1    0.00631    0.00544
Audi S5 Convertible 2012       3435         18    0.00491      0.944    0.00688    0.00608
    Audi S5 Coupe 2012       3435         18    0.00517          1    0.00603     0.0045
    Audi S4 Sedan 2012       3435         17    0.00473          1    0.00541    0.00399
    Audi S4 Sedan 2007       3435         19    0.00532          1     0.0066    0.00581
 Audi TT RS Coupe 2012       3435         17    0.00489          1    0.00498    0.00367
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00424          1     0.0218     0.0137
BMW 1 Series Convertible 2012       3435         15    0.00426          1    0.00704    0.00569
BMW 1 Series Coupe 2012       3435         17    0.00485          1     0.0228     0.0192
BMW 3 Series Sedan 2012       3435         18    0.00477      0.944     0.0082    0.00672
BMW 3 Series Wagon 2012       3435         17    0.00475          1     0.0063     0.0041
BMW 6 Series Convertible 2007       3435         19    0.00521          1     0.0061    0.00512
       BMW X5 SUV 2007       3435         17     0.0048          1    0.00686     0.0056
       BMW X6 SUV 2012       3435         18    0.00493          1     0.0191     0.0172
     BMW M3 Coupe 2012       3435         19    0.00541          1    0.00711    0.00619
     BMW M5 Sedan 2010       3435         17    0.00487          1    0.00979    0.00802
BMW M6 Convertible 2010       3435         17    0.00478          1    0.00653     0.0048
       BMW X3 SUV 2012       3435         17    0.00483          1    0.00877    0.00725
BMW Z4 Convertible 2012       3435         17    0.00489          1     0.0104    0.00932
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00436          1    0.00449    0.00347
Bentley Arnage Sedan 2009       3435         17    0.00485          1    0.00734    0.00592
Bentley Mulsanne Sedan 2011       3435         15    0.00424          1    0.00567    0.00458
Bentley Continental GT Coupe 2012       3435         15     0.0043          1     0.0151     0.0123
Bentley Continental GT Coupe 2007       3435         19    0.00499      0.947    0.00551    0.00443
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00529          1    0.00821    0.00695
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00395          1     0.0118    0.00915
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00531          1     0.0101    0.00745
   Buick Regal GS 2012       3435         14    0.00395          1    0.00473    0.00362
Buick Rainier SUV 2007       3435         18      0.005          1     0.0221     0.0152
Buick Verano Sedan 2012       3435         16     0.0045          1    0.00545    0.00441
Buick Enclave SUV 2012       3435         18    0.00489          1    0.00834    0.00683
Cadillac CTS-V Sedan 2012       3435         18    0.00515          1    0.00583    0.00411
 Cadillac SRX SUV 2012       3435         17     0.0048          1    0.00675    0.00548
Cadillac Escalade EXT Crew Cab 2007       3435         19     0.0055          1    0.00711    0.00549
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00452          1    0.00718    0.00654
Chevrolet Corvette Convertible 2012       3435         17    0.00488          1    0.00929    0.00744
Chevrolet Corvette ZR1 2012       3435         19    0.00528          1     0.0063    0.00535
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00464          1    0.00489    0.00361
Chevrolet Traverse SUV 2012       3435         19    0.00517          1     0.0071    0.00511
Chevrolet Camaro Convertible 2012       3435         19    0.00536          1    0.00792    0.00682
 Chevrolet HHR SS 2010       3435         15    0.00429          1    0.00475    0.00392
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00611    0.00499
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00451          1     0.0068    0.00527
Chevrolet Sonic Sedan 2012       3435         19    0.00543          1     0.0115    0.00868
Chevrolet Express Cargo Van 2007       3435         13     0.0037          1    0.00768     0.0066
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00534          1    0.00922     0.0079
Chevrolet Cobalt SS 2010       3435         17    0.00488          1    0.00595    0.00443
Chevrolet Malibu Hybrid Sedan 2010       3435         17    0.00489          1    0.00606     0.0049
Chevrolet TrailBlazer SS 2009       3435         16    0.00447          1    0.00473      0.004
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00451          1    0.00534    0.00436
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00516          1     0.0134     0.0116
Chevrolet Express Van 2007       3435         14    0.00394          1    0.00399    0.00346
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00519          1    0.00792    0.00603
Chevrolet Malibu Sedan 2007       3435         19     0.0054          1     0.0091    0.00756
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00527          1    0.00679    0.00509
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00546          1    0.00926    0.00715
Chrysler Aspen SUV 2009       3435         19    0.00532          1     0.0112    0.00949
Chrysler Sebring Convertible 2010       3435         17    0.00486          1    0.00921    0.00783
Chrysler Town and Country Minivan 2012       3435         16    0.00444          1    0.00471    0.00405
Chrysler 300 SRT-8 2010       3435         21    0.00596          1    0.00884    0.00716
Chrysler Crossfire Convertible 2008       3435         18    0.00512          1     0.0069    0.00532
Chrysler PT Cruiser Convertible 2008       3435         19    0.00543          1    0.00702    0.00547
Daewoo Nubira Wagon 2002       3435         19    0.00543          1     0.0072    0.00628
Dodge Caliber Wagon 2012       3435         17     0.0049          1    0.00651    0.00428
Dodge Caliber Wagon 2007       3435         18    0.00498          1    0.00585    0.00496
Dodge Caravan Minivan 1997       3435         19    0.00535          1    0.00634    0.00482
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00508          1    0.00642      0.005
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00546          1    0.00731    0.00572
Dodge Sprinter Cargo Van 2009       3435         17    0.00482          1    0.00529    0.00412
Dodge Journey SUV 2012       3435         19     0.0054          1      0.061     0.0545
Dodge Dakota Crew Cab 2010       3435         17    0.00476          1    0.00669    0.00508
Dodge Dakota Club Cab 2007       3435         17    0.00482          1     0.0061     0.0046
Dodge Magnum Wagon 2008       3435         16    0.00452          1    0.00735     0.0064
Dodge Challenger SRT8 2011       3435         17    0.00483          1    0.00529    0.00469
Dodge Durango SUV 2012       3435         19    0.00543          1    0.00789    0.00707
Dodge Durango SUV 2007       3435         19    0.00531          1    0.00639    0.00498
Dodge Charger Sedan 2012       3435         17     0.0048          1    0.00731    0.00608
Dodge Charger SRT-8 2009       3435         18    0.00503          1      0.007    0.00602
Eagle Talon Hatchback 1998       3435         19    0.00541          1     0.0136     0.0127
  FIAT 500 Abarth 2012       3435         12    0.00337          1    0.00442    0.00318
FIAT 500 Convertible 2012       3435         15     0.0042          1    0.00675    0.00603
 Ferrari FF Coupe 2012       3435         18    0.00506          1      0.011    0.00865
Ferrari California Convertible 2012       3435         17    0.00487          1    0.00694    0.00596
Ferrari 458 Italia Convertible 2012       3435         17    0.00479          1    0.00558    0.00399
Ferrari 458 Italia Coupe 2012       3435         18    0.00505          1    0.00581    0.00491
Fisker Karma Sedan 2012       3435         19    0.00529          1     0.0071    0.00526
Ford F-450 Super Duty Crew Cab 2012       3435         17    0.00476          1      0.012    0.00969
Ford Mustang Convertible 2007       3435         19    0.00542          1    0.00601    0.00448
Ford Freestar Minivan 2007       3435         19    0.00536          1    0.00579    0.00493
Ford Expedition EL SUV 2009       3435         19    0.00543          1    0.00579    0.00468
    Ford Edge SUV 2012       3435         18    0.00507          1    0.00944    0.00764
Ford Ranger SuperCab 2011       3435         18    0.00511          1    0.00525    0.00446
    Ford GT Coupe 2006       3435         19    0.00538          1    0.00631    0.00487
Ford F-150 Regular Cab 2012       3435         18    0.00515          1    0.00899    0.00698
Ford F-150 Regular Cab 2007       3435         19    0.00539          1     0.0087    0.00732
 Ford Focus Sedan 2007       3435         19     0.0053          1     0.0073    0.00632
Ford E-Series Wagon Van 2012       3435         16    0.00464          1    0.00543    0.00449
Ford Fiesta Sedan 2012       3435         18    0.00508          1    0.00713    0.00608
  GMC Terrain SUV 2012       3435         17    0.00474          1    0.00754    0.00638
   GMC Savana Van 2012       3435         28    0.00798          1     0.0128      0.011
GMC Yukon Hybrid SUV 2012       3435         18    0.00517          1    0.00727    0.00535
   GMC Acadia SUV 2012       3435         19    0.00524          1    0.00707    0.00424
GMC Canyon Extended Cab 2012       3435         16     0.0046          1    0.00572    0.00426
Geo Metro Convertible 1993       3435         19     0.0054          1    0.00694    0.00542
HUMMER H3T Crew Cab 2010       3435         17    0.00485          1    0.00756    0.00626
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00532          1     0.0064    0.00556
Honda Odyssey Minivan 2012       3435         18    0.00496          1    0.00524    0.00435
Honda Odyssey Minivan 2007       3435         17    0.00475          1    0.00596    0.00519
Honda Accord Coupe 2012       3435         17    0.00474          1    0.00525    0.00428
Honda Accord Sedan 2012       3435         17    0.00489          1    0.00761    0.00675
Hyundai Veloster Hatchback 2012       3435         17    0.00487          1    0.00572     0.0046
Hyundai Santa Fe SUV 2012       3435         18    0.00502          1    0.00759    0.00574
Hyundai Tucson SUV 2012       3435         19    0.00544          1    0.00969    0.00843
Hyundai Veracruz SUV 2012       3435         18    0.00498          1     0.0082    0.00701
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00434          1    0.00458    0.00382
Hyundai Elantra Sedan 2007       3435         18    0.00504          1    0.00823    0.00669
Hyundai Accent Sedan 2012       3435         11    0.00311          1    0.00559    0.00404
Hyundai Genesis Sedan 2012       3435         19    0.00544          1    0.00686    0.00577
Hyundai Sonata Sedan 2012       3435         17    0.00478          1    0.00579    0.00463
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00509          1    0.00898    0.00664
Hyundai Azera Sedan 2012       3435         18    0.00501          1    0.00636    0.00465
Infiniti G Coupe IPL 2012       3435         15     0.0043          1     0.0055    0.00389
Infiniti QX56 SUV 2011       3435         14    0.00397          1    0.00509    0.00462
Isuzu Ascender SUV 2008       3435         16    0.00446          1    0.00784    0.00668
    Jaguar XK XKR 2012       3435         19    0.00543          1     0.0134       0.01
 Jeep Patriot SUV 2012       3435         19     0.0054          1      0.017     0.0137
Jeep Wrangler SUV 2012       3435         18    0.00508          1    0.00894    0.00732
 Jeep Liberty SUV 2012       3435         19    0.00522          1     0.0208     0.0188
Jeep Grand Cherokee SUV 2012       3435         19    0.00529          1    0.00809    0.00636
 Jeep Compass SUV 2012       3435         18    0.00504          1    0.00924    0.00785
Lamborghini Reventon Coupe 2008       3435         15    0.00427          1    0.00499    0.00433
Lamborghini Aventador Coupe 2012       3435         19    0.00536          1    0.00666    0.00485
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00429          1     0.0055    0.00407
Lamborghini Diablo Coupe 2001       3435         19    0.00539          1     0.0165     0.0119
Land Rover Range Rover SUV 2012       3435         18    0.00502          1    0.00749    0.00635
Land Rover LR2 SUV 2012       3435         18    0.00513          1    0.00572    0.00469
Lincoln Town Car Sedan 2011       3435         17    0.00486          1    0.00528    0.00425
MINI Cooper Roadster Convertible 2012       3435         15    0.00427          1     0.0145     0.0122
Maybach Landaulet Convertible 2012       3435         13    0.00367          1    0.00431    0.00338
Mazda Tribute SUV 2011       3435         15    0.00419          1     0.0047    0.00354
McLaren MP4-12C Coupe 2012       3435         19    0.00545          1    0.00577    0.00506
Mercedes-Benz 300-Class Convertible 1993       3435         20     0.0055          1    0.00692    0.00526
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00524          1    0.00571     0.0042
Mercedes-Benz SL-Class Coupe 2009       3435         15     0.0041          1    0.00664    0.00541
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00519          1     0.0101    0.00846
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00539          1    0.00744    0.00622
Mercedes-Benz Sprinter Van 2012       3435         17    0.00466          1    0.00709    0.00617
Mitsubishi Lancer Sedan 2012       3435         20    0.00554          1    0.00582    0.00459
Nissan Leaf Hatchback 2012       3435         18    0.00509          1    0.00668    0.00554
Nissan NV Passenger Van 2012       3435         17    0.00469          1    0.00607    0.00479
Nissan Juke Hatchback 2012       3435         19    0.00537          1    0.00562     0.0046
Nissan 240SX Coupe 1998       3435         19    0.00536          1    0.00627    0.00499
Plymouth Neon Coupe 1999       3435         19    0.00531          1    0.00652    0.00553
Porsche Panamera Sedan 2012       3435         19    0.00545          1     0.0056     0.0046
Ram C-V Cargo Van Minivan 2012       3435         17    0.00493          1     0.0201     0.0162
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00366          1     0.0428     0.0379
Rolls-Royce Ghost Sedan 2012       3435         17    0.00468          1    0.00506    0.00388
Rolls-Royce Phantom Sedan 2012       3435         19     0.0055          1     0.0142     0.0107
Scion xD Hatchback 2012       3435         17     0.0049          1     0.0057    0.00493
Spyker C8 Convertible 2009       3435         19      0.005      0.947    0.00522    0.00429
  Spyker C8 Coupe 2009       3435         18    0.00506          1    0.00576    0.00448
Suzuki Aerio Sedan 2007       3435         16    0.00458          1    0.00593    0.00492
Suzuki Kizashi Sedan 2012       3435         19    0.00522          1    0.00949     0.0078
Suzuki SX4 Hatchback 2012       3435         18    0.00516          1    0.00677    0.00604
 Suzuki SX4 Sedan 2012       3435         17    0.00471          1     0.0127     0.0108
Tesla Model S Sedan 2012       3435         17    0.00482          1    0.00497    0.00384
Toyota Sequoia SUV 2012       3435         17    0.00471          1     0.0107    0.00856
Toyota Camry Sedan 2012       3435         19    0.00548          1    0.00743    0.00533
Toyota Corolla Sedan 2012       3435         19     0.0053          1    0.00581    0.00423
Toyota 4Runner SUV 2012       3435         17    0.00477          1     0.0149     0.0124
Volkswagen Golf Hatchback 2012       3435         18     0.0052          1    0.00831    0.00731
Volkswagen Golf Hatchback 1991       3435         19    0.00533          1    0.00551    0.00464
Volkswagen Beetle Hatchback 2012       3435         18    0.00498          1    0.00943     0.0085
Volvo C30 Hatchback 2012       3435         17    0.00465          1    0.00987    0.00812
  Volvo 240 Sedan 1993       3435         19    0.00527          1    0.00961    0.00847
   Volvo XC90 SUV 2007       3435         18    0.00504          1    0.00963    0.00857
smart fortwo Convertible 2012       3435         16    0.00455          1    0.00695    0.00578
Results saved to yolov5/runs/train/exp12

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 2 --cache --img-size 320 --batch 64 --workers 32

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=2, batch_size=64, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=32, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-266-g72cad39 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train.cache... 11226 images, 0 backgrounds, 0 corrupt: 100% 11226/11226 [00:00<?, ?it/s]
train: Caching images (2.2GB ram): 100% 11226/11226 [01:20<00:00, 139.94it/s]
val: Scanning /content/data/labels/val.cache... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:00<?, ?it/s]
val: Caching images (0.7GB ram): 100% 3435/3435 [00:33<00:00, 101.91it/s]

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp13/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp13
Starting training for 2 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/1      3.74G    0.05197    0.01909     0.1234         88        320: 100% 176/176 [01:03<00:00,  2.78it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.35it/s]
                   all       3435       3435    0.00359      0.991    0.00732    0.00467

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        1/1      6.84G    0.02558    0.01286      0.121         74        320: 100% 176/176 [00:59<00:00,  2.95it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.46it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00679

2 epochs completed in 0.046 hours.
Optimizer stripped from yolov5/runs/train/exp13/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp13/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp13/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.29it/s]
                   all       3435       3435    0.00494      0.999    0.00834    0.00678
AM General Hummer SUV 2000       3435         19    0.00546          1    0.00688    0.00521
   Acura RL Sedan 2012       3435         14    0.00398          1     0.0124    0.00992
   Acura TL Sedan 2012       3435         18    0.00517          1    0.00696    0.00549
  Acura TL Type-S 2008       3435         18    0.00493          1    0.00588    0.00475
  Acura TSX Sedan 2012       3435         17    0.00477          1    0.00643    0.00516
Acura Integra Type R 2001       3435         19    0.00516      0.947    0.00672    0.00501
Acura ZDX Hatchback 2012       3435         17    0.00477          1    0.00629    0.00475
Aston Martin V8 Vantage Convertible 2012       3435         19    0.00546          1    0.00899    0.00754
Aston Martin V8 Vantage Coupe 2012       3435         17    0.00473          1    0.00551    0.00401
Aston Martin Virage Convertible 2012       3435         14    0.00394          1      0.018      0.015
Aston Martin Virage Coupe 2012       3435         16     0.0045          1    0.00875    0.00673
Audi RS 4 Convertible 2008       3435         15    0.00417          1    0.00745    0.00645
    Audi A5 Coupe 2012       3435         17    0.00483          1      0.005    0.00389
   Audi TTS Coupe 2012       3435         18    0.00501          1    0.00644    0.00555
    Audi R8 Coupe 2012       3435         18    0.00503          1    0.00667    0.00538
    Audi V8 Sedan 1994       3435         19    0.00526          1    0.00776    0.00591
   Audi 100 Sedan 1994       3435         17    0.00474          1    0.00947    0.00833
   Audi 100 Wagon 1994       3435         18    0.00514          1    0.00552    0.00327
Audi TT Hatchback 2011       3435         17    0.00465          1    0.00651    0.00546
    Audi S6 Sedan 2011       3435         19    0.00535          1    0.00631    0.00544
Audi S5 Convertible 2012       3435         18    0.00491      0.944    0.00688    0.00608
    Audi S5 Coupe 2012       3435         18    0.00517          1    0.00603     0.0045
    Audi S4 Sedan 2012       3435         17    0.00473          1    0.00541    0.00399
    Audi S4 Sedan 2007       3435         19    0.00532          1     0.0066    0.00581
 Audi TT RS Coupe 2012       3435         17    0.00489          1    0.00498    0.00367
BMW ActiveHybrid 5 Sedan 2012       3435         15    0.00424          1     0.0218     0.0137
BMW 1 Series Convertible 2012       3435         15    0.00426          1    0.00704    0.00569
BMW 1 Series Coupe 2012       3435         17    0.00485          1     0.0228     0.0192
BMW 3 Series Sedan 2012       3435         18    0.00477      0.944     0.0082    0.00672
BMW 3 Series Wagon 2012       3435         17    0.00475          1     0.0063     0.0041
BMW 6 Series Convertible 2007       3435         19    0.00521          1     0.0061    0.00512
       BMW X5 SUV 2007       3435         17     0.0048          1    0.00686     0.0056
       BMW X6 SUV 2012       3435         18    0.00493          1     0.0191     0.0172
     BMW M3 Coupe 2012       3435         19    0.00541          1    0.00711    0.00619
     BMW M5 Sedan 2010       3435         17    0.00487          1    0.00979    0.00802
BMW M6 Convertible 2010       3435         17    0.00478          1    0.00653     0.0048
       BMW X3 SUV 2012       3435         17    0.00483          1    0.00877    0.00725
BMW Z4 Convertible 2012       3435         17    0.00489          1     0.0104    0.00932
Bentley Continental Supersports Conv. Convertible 2012       3435         15    0.00436          1    0.00449    0.00347
Bentley Arnage Sedan 2009       3435         17    0.00485          1    0.00734    0.00592
Bentley Mulsanne Sedan 2011       3435         15    0.00424          1    0.00567    0.00458
Bentley Continental GT Coupe 2012       3435         15     0.0043          1     0.0151     0.0123
Bentley Continental GT Coupe 2007       3435         19    0.00499      0.947    0.00551    0.00443
Bentley Continental Flying Spur Sedan 2007       3435         19    0.00529          1    0.00821    0.00695
Bugatti Veyron 16.4 Convertible 2009       3435         14    0.00395          1     0.0118    0.00915
Bugatti Veyron 16.4 Coupe 2009       3435         19    0.00531          1     0.0101    0.00745
   Buick Regal GS 2012       3435         14    0.00395          1    0.00473    0.00362
Buick Rainier SUV 2007       3435         18      0.005          1     0.0221     0.0152
Buick Verano Sedan 2012       3435         16     0.0045          1    0.00545    0.00441
Buick Enclave SUV 2012       3435         18    0.00489          1    0.00834    0.00683
Cadillac CTS-V Sedan 2012       3435         18    0.00515          1    0.00583    0.00411
 Cadillac SRX SUV 2012       3435         17     0.0048          1    0.00675    0.00548
Cadillac Escalade EXT Crew Cab 2007       3435         19     0.0055          1    0.00711    0.00549
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16    0.00452          1    0.00718    0.00654
Chevrolet Corvette Convertible 2012       3435         17    0.00488          1    0.00929    0.00744
Chevrolet Corvette ZR1 2012       3435         19    0.00528          1     0.0063    0.00535
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16    0.00464          1    0.00489    0.00361
Chevrolet Traverse SUV 2012       3435         19    0.00517          1     0.0071    0.00511
Chevrolet Camaro Convertible 2012       3435         19    0.00536          1    0.00792    0.00682
 Chevrolet HHR SS 2010       3435         15    0.00429          1    0.00475    0.00392
Chevrolet Impala Sedan 2007       3435         18    0.00521          1    0.00611    0.00499
Chevrolet Tahoe Hybrid SUV 2012       3435         16    0.00451          1     0.0068    0.00527
Chevrolet Sonic Sedan 2012       3435         19    0.00543          1     0.0115    0.00868
Chevrolet Express Cargo Van 2007       3435         13     0.0037          1    0.00768     0.0066
Chevrolet Avalanche Crew Cab 2012       3435         19    0.00534          1    0.00922     0.0079
Chevrolet Cobalt SS 2010       3435         17    0.00488          1    0.00595    0.00443
Chevrolet Malibu Hybrid Sedan 2010       3435         17    0.00489          1    0.00606     0.0049
Chevrolet TrailBlazer SS 2009       3435         16    0.00447          1    0.00473      0.004
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16    0.00451          1    0.00534    0.00436
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18    0.00516          1     0.0134     0.0116
Chevrolet Express Van 2007       3435         14    0.00394          1    0.00399    0.00346
Chevrolet Monte Carlo Coupe 2007       3435         19    0.00519          1    0.00792    0.00603
Chevrolet Malibu Sedan 2007       3435         19     0.0054          1     0.0091    0.00756
Chevrolet Silverado 1500 Extended Cab 2012       3435         19    0.00527          1    0.00679    0.00509
Chevrolet Silverado 1500 Regular Cab 2012       3435         19    0.00546          1    0.00926    0.00715
Chrysler Aspen SUV 2009       3435         19    0.00532          1     0.0112    0.00949
Chrysler Sebring Convertible 2010       3435         17    0.00486          1    0.00921    0.00783
Chrysler Town and Country Minivan 2012       3435         16    0.00444          1    0.00471    0.00405
Chrysler 300 SRT-8 2010       3435         21    0.00596          1    0.00884    0.00716
Chrysler Crossfire Convertible 2008       3435         18    0.00512          1     0.0069    0.00532
Chrysler PT Cruiser Convertible 2008       3435         19    0.00543          1    0.00702    0.00547
Daewoo Nubira Wagon 2002       3435         19    0.00543          1     0.0072    0.00628
Dodge Caliber Wagon 2012       3435         17     0.0049          1    0.00651    0.00428
Dodge Caliber Wagon 2007       3435         18    0.00498          1    0.00585    0.00496
Dodge Caravan Minivan 1997       3435         19    0.00535          1    0.00634    0.00482
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18    0.00508          1    0.00642      0.005
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19    0.00546          1    0.00731    0.00572
Dodge Sprinter Cargo Van 2009       3435         17    0.00482          1    0.00529    0.00412
Dodge Journey SUV 2012       3435         19     0.0054          1      0.061     0.0545
Dodge Dakota Crew Cab 2010       3435         17    0.00476          1    0.00669    0.00508
Dodge Dakota Club Cab 2007       3435         17    0.00482          1     0.0061     0.0046
Dodge Magnum Wagon 2008       3435         16    0.00452          1    0.00735     0.0064
Dodge Challenger SRT8 2011       3435         17    0.00483          1    0.00529    0.00469
Dodge Durango SUV 2012       3435         19    0.00543          1    0.00789    0.00707
Dodge Durango SUV 2007       3435         19    0.00531          1    0.00639    0.00498
Dodge Charger Sedan 2012       3435         17     0.0048          1    0.00731    0.00608
Dodge Charger SRT-8 2009       3435         18    0.00503          1      0.007    0.00602
Eagle Talon Hatchback 1998       3435         19    0.00541          1     0.0136     0.0127
  FIAT 500 Abarth 2012       3435         12    0.00337          1    0.00442    0.00318
FIAT 500 Convertible 2012       3435         15     0.0042          1    0.00675    0.00603
 Ferrari FF Coupe 2012       3435         18    0.00506          1      0.011    0.00865
Ferrari California Convertible 2012       3435         17    0.00487          1    0.00694    0.00596
Ferrari 458 Italia Convertible 2012       3435         17    0.00479          1    0.00558    0.00399
Ferrari 458 Italia Coupe 2012       3435         18    0.00505          1    0.00581    0.00491
Fisker Karma Sedan 2012       3435         19    0.00529          1     0.0071    0.00526
Ford F-450 Super Duty Crew Cab 2012       3435         17    0.00476          1      0.012    0.00969
Ford Mustang Convertible 2007       3435         19    0.00542          1    0.00601    0.00448
Ford Freestar Minivan 2007       3435         19    0.00536          1    0.00579    0.00493
Ford Expedition EL SUV 2009       3435         19    0.00543          1    0.00579    0.00468
    Ford Edge SUV 2012       3435         18    0.00507          1    0.00944    0.00764
Ford Ranger SuperCab 2011       3435         18    0.00511          1    0.00525    0.00446
    Ford GT Coupe 2006       3435         19    0.00538          1    0.00631    0.00487
Ford F-150 Regular Cab 2012       3435         18    0.00515          1    0.00899    0.00698
Ford F-150 Regular Cab 2007       3435         19    0.00539          1     0.0087    0.00732
 Ford Focus Sedan 2007       3435         19     0.0053          1     0.0073    0.00632
Ford E-Series Wagon Van 2012       3435         16    0.00464          1    0.00543    0.00449
Ford Fiesta Sedan 2012       3435         18    0.00508          1    0.00713    0.00608
  GMC Terrain SUV 2012       3435         17    0.00474          1    0.00754    0.00638
   GMC Savana Van 2012       3435         28    0.00798          1     0.0128      0.011
GMC Yukon Hybrid SUV 2012       3435         18    0.00517          1    0.00727    0.00535
   GMC Acadia SUV 2012       3435         19    0.00524          1    0.00707    0.00424
GMC Canyon Extended Cab 2012       3435         16     0.0046          1    0.00572    0.00426
Geo Metro Convertible 1993       3435         19     0.0054          1    0.00694    0.00542
HUMMER H3T Crew Cab 2010       3435         17    0.00485          1    0.00756    0.00626
HUMMER H2 SUT Crew Cab 2009       3435         19    0.00532          1     0.0064    0.00556
Honda Odyssey Minivan 2012       3435         18    0.00496          1    0.00524    0.00435
Honda Odyssey Minivan 2007       3435         17    0.00475          1    0.00596    0.00519
Honda Accord Coupe 2012       3435         17    0.00474          1    0.00525    0.00428
Honda Accord Sedan 2012       3435         17    0.00489          1    0.00761    0.00675
Hyundai Veloster Hatchback 2012       3435         17    0.00487          1    0.00572     0.0046
Hyundai Santa Fe SUV 2012       3435         18    0.00502          1    0.00759    0.00574
Hyundai Tucson SUV 2012       3435         19    0.00544          1    0.00969    0.00843
Hyundai Veracruz SUV 2012       3435         18    0.00498          1     0.0082    0.00701
Hyundai Sonata Hybrid Sedan 2012       3435         15    0.00434          1    0.00458    0.00382
Hyundai Elantra Sedan 2007       3435         18    0.00504          1    0.00823    0.00669
Hyundai Accent Sedan 2012       3435         11    0.00311          1    0.00559    0.00404
Hyundai Genesis Sedan 2012       3435         19    0.00544          1    0.00686    0.00577
Hyundai Sonata Sedan 2012       3435         17    0.00478          1    0.00579    0.00463
Hyundai Elantra Touring Hatchback 2012       3435         18    0.00509          1    0.00898    0.00664
Hyundai Azera Sedan 2012       3435         18    0.00501          1    0.00636    0.00465
Infiniti G Coupe IPL 2012       3435         15     0.0043          1     0.0055    0.00389
Infiniti QX56 SUV 2011       3435         14    0.00397          1    0.00509    0.00462
Isuzu Ascender SUV 2008       3435         16    0.00446          1    0.00784    0.00668
    Jaguar XK XKR 2012       3435         19    0.00543          1     0.0134       0.01
 Jeep Patriot SUV 2012       3435         19     0.0054          1      0.017     0.0137
Jeep Wrangler SUV 2012       3435         18    0.00508          1    0.00894    0.00732
 Jeep Liberty SUV 2012       3435         19    0.00522          1     0.0208     0.0188
Jeep Grand Cherokee SUV 2012       3435         19    0.00529          1    0.00809    0.00636
 Jeep Compass SUV 2012       3435         18    0.00504          1    0.00924    0.00785
Lamborghini Reventon Coupe 2008       3435         15    0.00427          1    0.00499    0.00433
Lamborghini Aventador Coupe 2012       3435         19    0.00536          1    0.00666    0.00485
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15    0.00429          1     0.0055    0.00407
Lamborghini Diablo Coupe 2001       3435         19    0.00539          1     0.0165     0.0119
Land Rover Range Rover SUV 2012       3435         18    0.00502          1    0.00749    0.00635
Land Rover LR2 SUV 2012       3435         18    0.00513          1    0.00572    0.00469
Lincoln Town Car Sedan 2011       3435         17    0.00486          1    0.00528    0.00425
MINI Cooper Roadster Convertible 2012       3435         15    0.00427          1     0.0145     0.0122
Maybach Landaulet Convertible 2012       3435         13    0.00367          1    0.00431    0.00338
Mazda Tribute SUV 2011       3435         15    0.00419          1     0.0047    0.00354
McLaren MP4-12C Coupe 2012       3435         19    0.00545          1    0.00577    0.00506
Mercedes-Benz 300-Class Convertible 1993       3435         20     0.0055          1    0.00692    0.00526
Mercedes-Benz C-Class Sedan 2012       3435         19    0.00524          1    0.00571     0.0042
Mercedes-Benz SL-Class Coupe 2009       3435         15     0.0041          1    0.00664    0.00541
Mercedes-Benz E-Class Sedan 2012       3435         19    0.00519          1     0.0101    0.00846
Mercedes-Benz S-Class Sedan 2012       3435         19    0.00539          1    0.00744    0.00622
Mercedes-Benz Sprinter Van 2012       3435         17    0.00466          1    0.00709    0.00617
Mitsubishi Lancer Sedan 2012       3435         20    0.00554          1    0.00582    0.00459
Nissan Leaf Hatchback 2012       3435         18    0.00509          1    0.00668    0.00554
Nissan NV Passenger Van 2012       3435         17    0.00469          1    0.00607    0.00479
Nissan Juke Hatchback 2012       3435         19    0.00537          1    0.00562     0.0046
Nissan 240SX Coupe 1998       3435         19    0.00536          1    0.00627    0.00499
Plymouth Neon Coupe 1999       3435         19    0.00531          1    0.00652    0.00553
Porsche Panamera Sedan 2012       3435         19    0.00545          1     0.0056     0.0046
Ram C-V Cargo Van Minivan 2012       3435         17    0.00493          1     0.0201     0.0162
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13    0.00366          1     0.0428     0.0379
Rolls-Royce Ghost Sedan 2012       3435         17    0.00468          1    0.00506    0.00388
Rolls-Royce Phantom Sedan 2012       3435         19     0.0055          1     0.0142     0.0107
Scion xD Hatchback 2012       3435         17     0.0049          1     0.0057    0.00493
Spyker C8 Convertible 2009       3435         19      0.005      0.947    0.00522    0.00429
  Spyker C8 Coupe 2009       3435         18    0.00506          1    0.00576    0.00448
Suzuki Aerio Sedan 2007       3435         16    0.00458          1    0.00593    0.00492
Suzuki Kizashi Sedan 2012       3435         19    0.00522          1    0.00949     0.0078
Suzuki SX4 Hatchback 2012       3435         18    0.00516          1    0.00677    0.00604
 Suzuki SX4 Sedan 2012       3435         17    0.00471          1     0.0127     0.0108
Tesla Model S Sedan 2012       3435         17    0.00482          1    0.00497    0.00384
Toyota Sequoia SUV 2012       3435         17    0.00471          1     0.0107    0.00856
Toyota Camry Sedan 2012       3435         19    0.00548          1    0.00743    0.00533
Toyota Corolla Sedan 2012       3435         19     0.0053          1    0.00581    0.00423
Toyota 4Runner SUV 2012       3435         17    0.00477          1     0.0149     0.0124
Volkswagen Golf Hatchback 2012       3435         18     0.0052          1    0.00831    0.00731
Volkswagen Golf Hatchback 1991       3435         19    0.00533          1    0.00551    0.00464
Volkswagen Beetle Hatchback 2012       3435         18    0.00498          1    0.00943     0.0085
Volvo C30 Hatchback 2012       3435         17    0.00465          1    0.00987    0.00812
  Volvo 240 Sedan 1993       3435         19    0.00527          1    0.00961    0.00847
   Volvo XC90 SUV 2007       3435         18    0.00504          1    0.00963    0.00857
smart fortwo Convertible 2012       3435         16    0.00455          1    0.00695    0.00578
Results saved to yolov5/runs/train/exp13

experiment = {
    '0': {'img_size': 640, 'batch_size': 16, 'workers': 8, 'cache': 'yes', 'epoch time in min': 8.47, 'GPU per epoch in GB': 8.87},
    '1': {'img_size': 320, 'batch_size': 16, 'workers': 8, 'cache': 'yes', 'epoch time in min': 1.39, 'GPU per epoch in GB': 2.1},

    '2': {'img_size': 320, 'batch_size': 32, 'workers': 8, 'cache': 'yes', 'epoch time in min': 1.10, 'GPU per epoch in GB': 3.24},
    '3': {'img_size': 320, 'batch_size': 64, 'workers': 8, 'cache': 'yes', 'epoch time in min': 1.03, 'GPU per epoch in GB': 6.84},

    '4': {'img_size': 320, 'batch_size': 64, 'workers': 8, 'cache': 'yes', 'epoch time in min': 1, 'GPU per epoch in GB': 6.84},
    '5': {'img_size': 320, 'batch_size': 64, 'workers': 8, 'cache': 'yes', 'epoch time in min': 0.59, 'GPU per epoch in GB': 6.84}
    }
pd.DataFrame(experiment).T

	img_size	batch_size	workers	cache	epoch time in min	GPU per epoch in GB
0	640	16	8	yes	8.47	8.87
1	320	16	8	yes	1.39	2.1
2	320	32	8	yes	1.1	3.24
3	320	64	8	yes	1.03	6.84
4	320	64	8	yes	1	6.84
5	320	64	8	yes	0.59	6.84

From the above exercise we finally reduced each epoch time to under a minute i.e. 59 secs and RAM of 6.84 GB of GPU.

• Best configuration: --batch 64 --img-size 320 --cache --workers 32

The Final Model

prepare_data_yolov5(train_split=0.7, val_split=0.2, test_split=0.1, first_n_classes=196)

     Total size: 16185
     Train size: 11226
Validation size: 3435
      Test size: 1524

	total_size	train_size	val_size	test_size
AM General Hummer SUV 2000	89	62	19	8
Acura RL Sedan 2012	64	44	14	6
Acura TL Sedan 2012	86	60	18	8
Acura TL Type-S 2008	84	58	18	8
Acura TSX Sedan 2012	81	56	17	8
...	...	...	...	...
Volkswagen Beetle Hatchback 2012	85	59	18	8
Volvo C30 Hatchback 2012	83	58	17	8
Volvo 240 Sedan 1993	91	63	19	9
Volvo XC90 SUV 2007	86	60	18	8
smart fortwo Convertible 2012	80	56	16	8

196 rows × 4 columns

!cat cars_data.yaml

# Dataset paths relative to the yolov5 folder 
train: ../data/images/train 
val:   ../data/images/val 
test:  ../data/images/test 

# Number of classes
nc: 196
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C-V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']

!python yolov5/train.py --data cars_data.yaml --weights yolov5s.pt --epochs 150 --batch 64 --img-size 320 --cache --workers 32

train: weights=yolov5s.pt, cfg=, data=cars_data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=150, batch_size=64, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=32, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.2-263-g0307954 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 2.88MB/s]
Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:01<00:00, 10.1MB/s]

Overriding model.yaml nc=80 with nc=196

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    542097  models.yolo.Detect                      [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7548241 parameters, 7548241 gradients, 17.6 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/data/labels/train... 11226 images, 0 backgrounds, 1 corrupt: 100% 11226/11226 [00:15<00:00, 738.89it/s]
train: WARNING ⚠️ /content/data/images/train/07389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [     1.1476]
train: New cache created: /content/data/labels/train.cache
train: Caching images (2.2GB ram): 100% 11225/11225 [01:36<00:00, 115.79it/s]
val: Scanning /content/data/labels/val... 3435 images, 0 backgrounds, 0 corrupt: 100% 3435/3435 [00:07<00:00, 481.56it/s]
val: New cache created: /content/data/labels/val.cache
val: Caching images (0.7GB ram): 100% 3435/3435 [00:38<00:00, 88.43it/s] 

AutoAnchor: 3.48 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to yolov5/runs/train/exp/labels.jpg... 
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to yolov5/runs/train/exp
Starting training for 150 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      0/149      3.74G    0.05207    0.01909     0.1234         85        320: 100% 176/176 [01:12<00:00,  2.42it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.17it/s]
                   all       3435       3435    0.00456      0.996    0.00828    0.00596

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      1/149      6.84G    0.02893    0.01284     0.1206         73        320: 100% 176/176 [01:08<00:00,  2.58it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.23it/s]
                   all       3435       3435    0.00497      0.999    0.00866    0.00746

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      2/149      6.84G    0.02461     0.0104     0.1194         62        320: 100% 176/176 [01:07<00:00,  2.61it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.42it/s]
                   all       3435       3435    0.00502      0.998    0.00912     0.0073

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      3/149      6.84G    0.02001   0.009127     0.1188         75        320: 100% 176/176 [01:08<00:00,  2.55it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.27it/s]
                   all       3435       3435    0.00506      0.998     0.0101    0.00821

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      4/149      6.84G    0.01734   0.008542     0.1184         69        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:25<00:00,  1.07it/s]
                   all       3435       3435    0.00506      0.998     0.0137      0.012

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      5/149      6.84G    0.01623   0.008232     0.1179         73        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.30it/s]
                   all       3435       3435    0.00508      0.999     0.0184     0.0158

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      6/149      6.84G    0.01581   0.008007     0.1172         71        320: 100% 176/176 [01:11<00:00,  2.47it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.38it/s]
                   all       3435       3435    0.00507      0.999     0.0192      0.017

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      7/149      6.84G    0.01544    0.00787     0.1161         66        320: 100% 176/176 [01:05<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.21it/s]
                   all       3435       3435    0.00509          1     0.0227     0.0199

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      8/149      6.84G    0.01545   0.007768     0.1144         64        320: 100% 176/176 [01:07<00:00,  2.61it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.22it/s]
                   all       3435       3435    0.00508      0.999     0.0236     0.0207

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
      9/149      6.84G    0.01549   0.007779     0.1128         73        320: 100% 176/176 [01:07<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.39it/s]
                   all       3435       3435    0.00507      0.999     0.0286     0.0251

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     10/149      6.84G    0.01573   0.007871     0.1108         76        320: 100% 176/176 [01:12<00:00,  2.43it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.22it/s]
                   all       3435       3435      0.599       0.21     0.0385     0.0332

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     11/149      6.84G    0.01613   0.007927     0.1088         67        320: 100% 176/176 [01:03<00:00,  2.79it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:24<00:00,  1.12it/s]
                   all       3435       3435      0.355      0.362     0.0418     0.0361

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     12/149      6.84G    0.01635    0.00795     0.1072         62        320: 100% 176/176 [01:06<00:00,  2.64it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.219      0.423     0.0526     0.0466

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     13/149      6.84G    0.01636   0.007937     0.1056         69        320: 100% 176/176 [01:09<00:00,  2.53it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.18it/s]
                   all       3435       3435    0.00969      0.997     0.0606     0.0543

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     14/149      6.84G    0.01631   0.007909     0.1044         72        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.23it/s]
                   all       3435       3435      0.747     0.0873     0.0748     0.0661

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     15/149      6.84G    0.01641   0.007889     0.1031         65        320: 100% 176/176 [01:07<00:00,  2.62it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.23it/s]
                   all       3435       3435      0.771     0.0855     0.0811     0.0717

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     16/149      6.84G    0.01666   0.007894     0.1015         66        320: 100% 176/176 [01:07<00:00,  2.61it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.44it/s]
                   all       3435       3435      0.572       0.16     0.0944     0.0845

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     17/149      6.84G    0.01653   0.007951    0.09978         64        320: 100% 176/176 [01:10<00:00,  2.48it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.26it/s]
                   all       3435       3435      0.485      0.201      0.107      0.096

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     18/149      6.84G    0.01653   0.008024    0.09845         71        320: 100% 176/176 [01:03<00:00,  2.77it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.25it/s]
                   all       3435       3435      0.551      0.219      0.125      0.111

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     19/149      6.84G    0.01655   0.007894     0.0972         63        320: 100% 176/176 [01:09<00:00,  2.55it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.35it/s]
                   all       3435       3435      0.457      0.269      0.136      0.121

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     20/149      6.84G    0.01676   0.008041    0.09586         79        320: 100% 176/176 [01:06<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.41it/s]
                   all       3435       3435      0.384      0.302      0.156       0.14

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     21/149      6.84G    0.01666   0.008026    0.09471         81        320: 100% 176/176 [01:08<00:00,  2.57it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.26it/s]
                   all       3435       3435      0.443      0.312      0.197      0.177

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     22/149      6.84G    0.01674    0.00802    0.09341         71        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:24<00:00,  1.11it/s]
                   all       3435       3435      0.466      0.269      0.178      0.158

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     23/149      6.84G    0.01693     0.0081    0.09197         69        320: 100% 176/176 [01:05<00:00,  2.70it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.446      0.355      0.223        0.2

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     24/149      6.84G    0.01708   0.008108    0.09036         65        320: 100% 176/176 [01:10<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.48it/s]
                   all       3435       3435      0.381      0.361      0.213       0.19

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     25/149      6.84G     0.0172   0.008204    0.08912         67        320: 100% 176/176 [01:06<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.36it/s]
                   all       3435       3435      0.424      0.371      0.256      0.231

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     26/149      6.84G    0.01715   0.008149    0.08798         66        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.29it/s]
                   all       3435       3435      0.418      0.424      0.309      0.279

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     27/149      6.84G    0.01739    0.00805    0.08667         62        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.29it/s]
                   all       3435       3435      0.406      0.454      0.334      0.302

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     28/149      6.84G    0.01737   0.008152     0.0856         74        320: 100% 176/176 [01:10<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.46it/s]
                   all       3435       3435      0.397      0.456      0.345       0.31

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     29/149      6.84G    0.01762   0.008204    0.08426         66        320: 100% 176/176 [01:06<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.48it/s]
                   all       3435       3435      0.426      0.473      0.391      0.354

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     30/149      6.84G    0.01759   0.008082    0.08294         69        320: 100% 176/176 [01:09<00:00,  2.52it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.29it/s]
                   all       3435       3435      0.434      0.492      0.415      0.376

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     31/149      6.84G    0.01763   0.008188    0.08213         67        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.13it/s]
                   all       3435       3435      0.441      0.509      0.426      0.386

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     32/149      6.84G     0.0177   0.008194    0.08072         67        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.29it/s]
                   all       3435       3435      0.471      0.519      0.446      0.404

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     33/149      6.84G    0.01785   0.008164    0.07954         66        320: 100% 176/176 [01:04<00:00,  2.72it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.22it/s]
                   all       3435       3435      0.459      0.563      0.493      0.447

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     34/149      6.84G    0.01788   0.008135    0.07867         73        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.49it/s]
                   all       3435       3435      0.474      0.582      0.512      0.466

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     35/149      6.84G    0.01797   0.008155    0.07732         73        320: 100% 176/176 [01:10<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.39it/s]
                   all       3435       3435      0.471      0.594      0.532      0.482

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     36/149      6.84G    0.01798   0.008217    0.07635         53        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.492      0.617      0.565      0.516

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     37/149      6.84G    0.01812   0.008226    0.07543         63        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.506      0.636      0.579      0.527

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     38/149      6.84G    0.01819   0.008239    0.07368         66        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.516      0.641      0.593       0.54

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     39/149      6.84G    0.01822   0.008228    0.07317         67        320: 100% 176/176 [01:08<00:00,  2.56it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.41it/s]
                   all       3435       3435      0.505      0.687      0.622      0.567

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     40/149      6.84G    0.01813    0.00824    0.07209         62        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.54it/s]
                   all       3435       3435      0.518      0.677      0.635       0.58

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     41/149      6.84G    0.01819   0.008277    0.07094         76        320: 100% 176/176 [01:10<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.52it/s]
                   all       3435       3435       0.55      0.692      0.656      0.601

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     42/149      6.84G    0.01811   0.008233    0.07013         76        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.46it/s]
                   all       3435       3435       0.55      0.709      0.675       0.62

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     43/149      6.84G    0.01836   0.008153    0.06959         62        320: 100% 176/176 [01:07<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.552      0.725      0.683      0.626

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     44/149      6.84G    0.01825   0.008176    0.06804         69        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.21it/s]
                   all       3435       3435      0.586      0.726      0.702      0.643

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     45/149      6.84G     0.0184   0.008101    0.06717         63        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435       0.59      0.738      0.717       0.66

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     46/149      6.84G    0.01844   0.008188    0.06658         75        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.15it/s]
                   all       3435       3435      0.607      0.741      0.733      0.673

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     47/149      6.84G     0.0185   0.008191     0.0656         70        320: 100% 176/176 [01:04<00:00,  2.74it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.42it/s]
                   all       3435       3435      0.614      0.751      0.746      0.686

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     48/149      6.84G    0.01836   0.008205    0.06504         67        320: 100% 176/176 [01:07<00:00,  2.62it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.38it/s]
                   all       3435       3435      0.634      0.763       0.76      0.699

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     49/149      6.84G    0.01842   0.008206    0.06378         66        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.53it/s]
                   all       3435       3435       0.66      0.757      0.774      0.711

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     50/149      6.84G    0.01836   0.008194    0.06314         67        320: 100% 176/176 [01:10<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.39it/s]
                   all       3435       3435       0.67      0.769      0.776      0.714

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     51/149      6.84G    0.01854   0.008123    0.06188         60        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.665      0.794      0.792      0.731

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     52/149      6.84G    0.01821   0.008196    0.06161         71        320: 100% 176/176 [01:05<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.709      0.786      0.807      0.746

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     53/149      6.84G    0.01833    0.00814    0.06052         74        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435       0.72      0.792      0.811      0.749

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     54/149      6.84G    0.01847   0.008121    0.06014         78        320: 100% 176/176 [01:05<00:00,  2.70it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.722        0.8       0.82      0.757

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     55/149      6.84G    0.01836   0.008134    0.05929         62        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.738      0.807      0.831      0.769

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     56/149      6.84G    0.01837   0.008067    0.05852         70        320: 100% 176/176 [01:07<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.45it/s]
                   all       3435       3435      0.743      0.805      0.836      0.774

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     57/149      6.84G    0.01835   0.008115    0.05765         78        320: 100% 176/176 [01:05<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.48it/s]
                   all       3435       3435      0.757      0.808      0.844      0.781

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     58/149      6.84G    0.01834   0.008066    0.05758         77        320: 100% 176/176 [01:09<00:00,  2.53it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.54it/s]
                   all       3435       3435      0.766      0.814      0.847      0.784

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     59/149      6.84G    0.01824   0.008079    0.05623         74        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.55it/s]
                   all       3435       3435      0.765      0.823      0.855      0.794

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     60/149      6.84G    0.01805   0.008055    0.05605         70        320: 100% 176/176 [01:09<00:00,  2.53it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.50it/s]
                   all       3435       3435      0.767      0.826       0.86      0.796

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     61/149      6.84G     0.0182    0.00807    0.05532         63        320: 100% 176/176 [01:03<00:00,  2.75it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.43it/s]
                   all       3435       3435      0.763      0.845      0.865      0.801

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     62/149      6.84G    0.01819   0.007989    0.05496         63        320: 100% 176/176 [01:06<00:00,  2.64it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.764      0.852       0.87      0.806

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     63/149      6.84G    0.01798   0.008099    0.05395         71        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.26it/s]
                   all       3435       3435      0.791      0.838      0.875      0.811

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     64/149      6.84G    0.01801   0.008003     0.0539         65        320: 100% 176/176 [01:03<00:00,  2.79it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.797      0.838      0.881      0.817

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     65/149      6.84G    0.01803   0.008048    0.05257         74        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.16it/s]
                   all       3435       3435      0.808      0.842      0.885      0.821

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     66/149      6.84G    0.01815   0.007948    0.05177         71        320: 100% 176/176 [01:01<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.805      0.853      0.886      0.823

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     67/149      6.84G    0.01791   0.007999    0.05167         72        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.16it/s]
                   all       3435       3435      0.804      0.857      0.889      0.826

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     68/149      6.84G    0.01793    0.00796    0.05101         66        320: 100% 176/176 [01:01<00:00,  2.87it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.809      0.859      0.893       0.83

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     69/149      6.84G    0.01789   0.007956    0.05044         74        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:23<00:00,  1.15it/s]
                   all       3435       3435      0.814      0.863      0.896      0.833

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     70/149      6.84G    0.01788   0.008014    0.04995         65        320: 100% 176/176 [01:03<00:00,  2.77it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.37it/s]
                   all       3435       3435      0.813      0.865        0.9      0.838

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     71/149      6.84G    0.01778   0.007991    0.04922         69        320: 100% 176/176 [01:04<00:00,  2.73it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.23it/s]
                   all       3435       3435      0.811      0.869      0.903      0.841

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     72/149      6.84G    0.01765   0.007955    0.04843         64        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.49it/s]
                   all       3435       3435      0.808      0.881      0.906      0.843

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     73/149      6.84G    0.01778   0.007944     0.0478         70        320: 100% 176/176 [01:07<00:00,  2.60it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.43it/s]
                   all       3435       3435      0.817      0.881       0.91      0.847

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     74/149      6.84G    0.01775   0.007856    0.04771         73        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.52it/s]
                   all       3435       3435      0.818      0.885      0.911      0.848

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     75/149      6.84G    0.01753   0.007937    0.04775         68        320: 100% 176/176 [01:09<00:00,  2.55it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.54it/s]
                   all       3435       3435      0.817      0.893      0.913       0.85

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     76/149      6.84G    0.01757     0.0079    0.04673         68        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.51it/s]
                   all       3435       3435      0.816      0.895      0.915      0.852

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     77/149      6.84G    0.01745   0.007896    0.04538         69        320: 100% 176/176 [01:08<00:00,  2.58it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.814      0.903      0.916      0.853

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     78/149      6.84G    0.01738   0.007802    0.04574         81        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.813       0.91      0.919      0.856

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     79/149      6.84G    0.01745   0.007817      0.045         68        320: 100% 176/176 [01:05<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.829      0.903      0.921      0.858

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     80/149      6.84G    0.01722    0.00783    0.04502         73        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.832      0.903      0.923      0.859

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     81/149      6.84G     0.0172   0.007843    0.04431         67        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.832      0.913      0.925      0.861

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     82/149      6.84G    0.01708   0.007804    0.04273         74        320: 100% 176/176 [01:01<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.838       0.91      0.926      0.863

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     83/149      6.84G     0.0172   0.007713    0.04317         76        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435       0.84       0.91      0.928      0.865

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     84/149      6.84G    0.01703   0.007701    0.04262         64        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.844      0.913      0.929      0.866

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     85/149      6.84G    0.01701   0.007686    0.04217         69        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.847      0.915       0.93      0.866

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     86/149      6.84G    0.01694   0.007643    0.04226         65        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.849      0.913       0.93      0.866

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     87/149      6.84G    0.01686   0.007717     0.0421         75        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.852      0.911      0.931      0.868

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     88/149      6.84G    0.01676   0.007746    0.04159         66        320: 100% 176/176 [01:01<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.857      0.912      0.932      0.869

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     89/149      6.84G    0.01666   0.007649     0.0407         60        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.856      0.914      0.933       0.87

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     90/149      6.84G    0.01667   0.007619    0.04034         59        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.852       0.92      0.934      0.871

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     91/149      6.84G    0.01653    0.00767    0.03973         69        320: 100% 176/176 [01:06<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.848      0.923      0.935      0.872

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     92/149      6.84G    0.01648   0.007555    0.03997         72        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.849      0.922      0.936      0.873

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     93/149      6.84G    0.01658   0.007615    0.03917         62        320: 100% 176/176 [01:06<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.859       0.92      0.937      0.874

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     94/149      6.84G    0.01637   0.007495     0.0389         73        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.855      0.922      0.938      0.875

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     95/149      6.84G    0.01632   0.007522    0.03811         79        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.855      0.925      0.938      0.875

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     96/149      6.84G    0.01621   0.007584    0.03812         74        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.857      0.924      0.939      0.876

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     97/149      6.84G    0.01616   0.007536    0.03758         70        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.859      0.924       0.94      0.877

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     98/149      6.84G    0.01593   0.007507    0.03776         71        320: 100% 176/176 [01:02<00:00,  2.81it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435       0.86      0.926       0.94      0.878

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
     99/149      6.84G    0.01608   0.007422    0.03714         71        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.861      0.928      0.941      0.878

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    100/149      6.84G    0.01587   0.007423    0.03677         67        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.862      0.929      0.941      0.879

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    101/149      6.84G    0.01591   0.007497    0.03598         75        320: 100% 176/176 [01:06<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.865      0.927      0.942       0.88

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    102/149      6.84G    0.01579    0.00745    0.03554         72        320: 100% 176/176 [01:02<00:00,  2.80it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.865      0.929      0.942       0.88

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    103/149      6.84G    0.01549   0.007385     0.0361         75        320: 100% 176/176 [01:06<00:00,  2.65it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.869      0.928      0.943       0.88

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    104/149      6.84G    0.01559   0.007317    0.03523         63        320: 100% 176/176 [01:01<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.869      0.931      0.943      0.881

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    105/149      6.84G    0.01544   0.007346    0.03525         79        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.871      0.929      0.944      0.881

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    106/149      6.84G     0.0154   0.007348    0.03462         72        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.875      0.925      0.944      0.882

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    107/149      6.84G    0.01529   0.007362    0.03376         73        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.876      0.925      0.944      0.882

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    108/149      6.84G    0.01522   0.007318    0.03345         67        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.879      0.925      0.945      0.882

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    109/149      6.84G    0.01505   0.007209    0.03361         65        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435       0.88      0.925      0.945      0.882

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    110/149      6.84G    0.01498    0.00722     0.0334         73        320: 100% 176/176 [01:01<00:00,  2.87it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.882      0.925      0.945      0.883

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    111/149      6.84G    0.01494   0.007216    0.03291         56        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.31it/s]
                   all       3435       3435      0.882      0.926      0.946      0.883

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    112/149      6.84G    0.01492   0.007262    0.03264         72        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.882      0.928      0.946      0.884

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    113/149      6.84G    0.01487   0.007187    0.03253         69        320: 100% 176/176 [01:05<00:00,  2.69it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.882      0.928      0.947      0.884

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    114/149      6.84G    0.01473   0.007116    0.03248         64        320: 100% 176/176 [01:02<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.882       0.93      0.947      0.884

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    115/149      6.84G    0.01457   0.007123    0.03169         77        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.883       0.93      0.947      0.885

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    116/149      6.84G     0.0146   0.007207    0.03176         73        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.41it/s]
                   all       3435       3435      0.884      0.931      0.948      0.885

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    117/149      6.84G    0.01439   0.007073    0.03109         71        320: 100% 176/176 [01:06<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.38it/s]
                   all       3435       3435      0.884      0.931      0.948      0.885

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    118/149      6.84G    0.01439    0.00701    0.03041         75        320: 100% 176/176 [01:02<00:00,  2.80it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.40it/s]
                   all       3435       3435      0.885      0.932      0.948      0.886

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    119/149      6.84G    0.01421   0.007046    0.03025         61        320: 100% 176/176 [01:06<00:00,  2.63it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.885      0.933      0.948      0.886

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    120/149      6.84G    0.01406   0.006985    0.02985         61        320: 100% 176/176 [01:01<00:00,  2.86it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.36it/s]
                   all       3435       3435      0.886      0.932      0.949      0.886

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    121/149      6.84G    0.01395   0.007008    0.03026         74        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.887      0.932      0.949      0.886

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    122/149      6.84G    0.01388   0.006962    0.02956         65        320: 100% 176/176 [01:02<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.35it/s]
                   all       3435       3435      0.888      0.934      0.949      0.887

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    123/149      6.84G    0.01384   0.006966    0.02912         71        320: 100% 176/176 [01:06<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.888      0.934      0.949      0.887

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    124/149      6.84G    0.01362   0.006926    0.02854         65        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.35it/s]
                   all       3435       3435      0.888      0.934       0.95      0.887

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    125/149      6.84G    0.01349   0.006863      0.028         67        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.888      0.935       0.95      0.887

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    126/149      6.84G    0.01353   0.006859    0.02855         75        320: 100% 176/176 [01:02<00:00,  2.84it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.886      0.935       0.95      0.887

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    127/149      6.84G    0.01338   0.006855    0.02794         70        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.886      0.936       0.95      0.888

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    128/149      6.84G    0.01316   0.006849    0.02769         68        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.887      0.937      0.951      0.888

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    129/149      6.84G    0.01304   0.006842    0.02719         68        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.887      0.937      0.951      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    130/149      6.84G    0.01275   0.006755    0.02712         78        320: 100% 176/176 [01:01<00:00,  2.85it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.888      0.937      0.951      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    131/149      6.84G    0.01277   0.006741    0.02692         75        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.887      0.937      0.951      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    132/149      6.84G    0.01274   0.006807    0.02671         68        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.888      0.936      0.952      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    133/149      6.84G    0.01251   0.006705    0.02618         73        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.889      0.936      0.952      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    134/149      6.84G     0.0123   0.006668    0.02578         68        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.888      0.937      0.952      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    135/149      6.84G    0.01234   0.006671     0.0258         73        320: 100% 176/176 [01:05<00:00,  2.67it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.888      0.938      0.952      0.889

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    136/149      6.84G    0.01213   0.006621    0.02523         69        320: 100% 176/176 [01:02<00:00,  2.83it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.33it/s]
                   all       3435       3435      0.888      0.938      0.953       0.89

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    137/149      6.84G    0.01204   0.006629    0.02533         66        320: 100% 176/176 [01:07<00:00,  2.62it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.34it/s]
                   all       3435       3435      0.888      0.938      0.953       0.89

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    138/149      6.84G    0.01186   0.006576    0.02475         67        320: 100% 176/176 [01:02<00:00,  2.82it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.889      0.938      0.953       0.89

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    139/149      6.84G     0.0118   0.006571    0.02459         72        320: 100% 176/176 [01:08<00:00,  2.56it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.42it/s]
                   all       3435       3435       0.89      0.939      0.953      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    140/149      6.84G    0.01156   0.006469    0.02484         60        320: 100% 176/176 [01:03<00:00,  2.76it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:19<00:00,  1.42it/s]
                   all       3435       3435      0.891      0.939      0.953      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    141/149      6.84G     0.0115    0.00653     0.0239         68        320: 100% 176/176 [01:10<00:00,  2.51it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.51it/s]
                   all       3435       3435      0.892      0.938      0.954      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    142/149      6.84G    0.01119   0.006435    0.02389         62        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.55it/s]
                   all       3435       3435      0.892      0.938      0.954      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    143/149      6.84G    0.01107   0.006436    0.02359         76        320: 100% 176/176 [01:10<00:00,  2.49it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.56it/s]
                   all       3435       3435      0.893      0.937      0.954      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    144/149      6.84G    0.01094   0.006398    0.02334         77        320: 100% 176/176 [01:05<00:00,  2.68it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.53it/s]
                   all       3435       3435      0.893      0.937      0.954      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    145/149      6.84G    0.01079   0.006333    0.02345         72        320: 100% 176/176 [01:10<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.54it/s]
                   all       3435       3435      0.894      0.937      0.954      0.891

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    146/149      6.84G    0.01071   0.006317    0.02306         58        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:17<00:00,  1.56it/s]
                   all       3435       3435      0.895      0.938      0.954      0.892

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    147/149      6.84G    0.01054    0.00636    0.02254         74        320: 100% 176/176 [01:10<00:00,  2.50it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:18<00:00,  1.49it/s]
                   all       3435       3435      0.896      0.938      0.954      0.892

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    148/149      6.84G    0.01026   0.006167    0.02259         72        320: 100% 176/176 [01:08<00:00,  2.55it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:22<00:00,  1.23it/s]
                   all       3435       3435      0.896      0.938      0.954      0.892

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    149/149      6.84G    0.01012   0.006279    0.02226         71        320: 100% 176/176 [01:06<00:00,  2.66it/s]
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:20<00:00,  1.32it/s]
                   all       3435       3435      0.896      0.938      0.954      0.892

150 epochs completed in 3.615 hours.
Optimizer stripped from yolov5/runs/train/exp/weights/last.pt, 15.3MB
Optimizer stripped from yolov5/runs/train/exp/weights/best.pt, 15.3MB

Validating yolov5/runs/train/exp/weights/best.pt...
Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 27/27 [00:21<00:00,  1.27it/s]
                   all       3435       3435      0.896      0.939      0.954      0.892
AM General Hummer SUV 2000       3435         19      0.999          1      0.995      0.871
   Acura RL Sedan 2012       3435         14       0.92      0.786      0.848      0.823
   Acura TL Sedan 2012       3435         18      0.784          1      0.951      0.877
  Acura TL Type-S 2008       3435         18      0.959          1      0.995      0.968
  Acura TSX Sedan 2012       3435         17      0.963      0.824       0.93      0.896
Acura Integra Type R 2001       3435         19      0.918          1      0.977      0.949
Acura ZDX Hatchback 2012       3435         17      0.972          1      0.995      0.956
Aston Martin V8 Vantage Convertible 2012       3435         19      0.768          1      0.956      0.892
Aston Martin V8 Vantage Coupe 2012       3435         17      0.869      0.882      0.899      0.775
Aston Martin Virage Convertible 2012       3435         14      0.736      0.796       0.82      0.792
Aston Martin Virage Coupe 2012       3435         16      0.829      0.907      0.898      0.874
Audi RS 4 Convertible 2008       3435         15      0.936      0.933      0.964      0.918
    Audi A5 Coupe 2012       3435         17      0.658      0.882      0.791      0.783
   Audi TTS Coupe 2012       3435         18      0.516      0.611      0.612      0.596
    Audi R8 Coupe 2012       3435         18      0.942      0.903      0.955      0.913
    Audi V8 Sedan 1994       3435         19       0.69      0.842      0.844      0.786
   Audi 100 Sedan 1994       3435         17      0.634      0.612      0.797      0.745
   Audi 100 Wagon 1994       3435         18      0.791      0.833      0.937      0.793
Audi TT Hatchback 2011       3435         17      0.589      0.674      0.579      0.555
    Audi S6 Sedan 2011       3435         19      0.649          1      0.912      0.836
Audi S5 Convertible 2012       3435         18      0.874      0.944      0.962      0.903
    Audi S5 Coupe 2012       3435         18      0.734      0.722      0.754      0.648
    Audi S4 Sedan 2012       3435         17      0.923      0.704      0.901      0.857
    Audi S4 Sedan 2007       3435         19      0.832      0.895       0.93      0.874
 Audi TT RS Coupe 2012       3435         17      0.708      0.857      0.848      0.798
BMW ActiveHybrid 5 Sedan 2012       3435         15      0.858      0.933       0.88      0.829
BMW 1 Series Convertible 2012       3435         15      0.963          1      0.995      0.967
BMW 1 Series Coupe 2012       3435         17      0.967          1      0.995      0.819
BMW 3 Series Sedan 2012       3435         18      0.843      0.833      0.937      0.867
BMW 3 Series Wagon 2012       3435         17      0.906      0.941      0.967      0.737
BMW 6 Series Convertible 2007       3435         19      0.797      0.842       0.94      0.888
       BMW X5 SUV 2007       3435         17      0.961          1      0.995        0.9
       BMW X6 SUV 2012       3435         18          1      0.972      0.995      0.978
     BMW M3 Coupe 2012       3435         19       0.88          1      0.995      0.964
     BMW M5 Sedan 2010       3435         17      0.886          1      0.995       0.96
BMW M6 Convertible 2010       3435         17      0.717      0.882      0.845      0.792
       BMW X3 SUV 2012       3435         17       0.96          1      0.995      0.968
BMW Z4 Convertible 2012       3435         17      0.879          1      0.952      0.891
Bentley Continental Supersports Conv. Convertible 2012       3435         15      0.929      0.876       0.94      0.856
Bentley Arnage Sedan 2009       3435         17      0.953          1      0.995      0.946
Bentley Mulsanne Sedan 2011       3435         15      0.928          1      0.991      0.956
Bentley Continental GT Coupe 2012       3435         15        0.8      0.867      0.886      0.834
Bentley Continental GT Coupe 2007       3435         19      0.811      0.842      0.865      0.796
Bentley Continental Flying Spur Sedan 2007       3435         19      0.825      0.995      0.939      0.871
Bugatti Veyron 16.4 Convertible 2009       3435         14      0.863      0.899      0.946       0.85
Bugatti Veyron 16.4 Coupe 2009       3435         19      0.867      0.947      0.975      0.915
   Buick Regal GS 2012       3435         14      0.879          1      0.995      0.985
Buick Rainier SUV 2007       3435         18      0.941          1      0.992      0.965
Buick Verano Sedan 2012       3435         16      0.981          1      0.995      0.943
Buick Enclave SUV 2012       3435         18      0.969          1      0.995      0.962
Cadillac CTS-V Sedan 2012       3435         18      0.952          1      0.995      0.959
 Cadillac SRX SUV 2012       3435         17      0.969          1      0.995      0.932
Cadillac Escalade EXT Crew Cab 2007       3435         19      0.854      0.947       0.96       0.83
Chevrolet Silverado 1500 Hybrid Crew Cab 2012       3435         16      0.587      0.938      0.664      0.621
Chevrolet Corvette Convertible 2012       3435         17      0.849      0.941      0.972      0.907
Chevrolet Corvette ZR1 2012       3435         19      0.808          1      0.983      0.904
Chevrolet Corvette Ron Fellows Edition Z06 2007       3435         16      0.937      0.923      0.984      0.873
Chevrolet Traverse SUV 2012       3435         19      0.947          1      0.995      0.944
Chevrolet Camaro Convertible 2012       3435         19          1      0.927      0.968      0.874
 Chevrolet HHR SS 2010       3435         15      0.961      0.933      0.944       0.87
Chevrolet Impala Sedan 2007       3435         18      0.707      0.889      0.924      0.904
Chevrolet Tahoe Hybrid SUV 2012       3435         16      0.791      0.875      0.904       0.89
Chevrolet Sonic Sedan 2012       3435         19      0.927          1      0.995      0.861
Chevrolet Express Cargo Van 2007       3435         13      0.538      0.272      0.666      0.633
Chevrolet Avalanche Crew Cab 2012       3435         19      0.902      0.974       0.99      0.943
Chevrolet Cobalt SS 2010       3435         17       0.91      0.941      0.946      0.804
Chevrolet Malibu Hybrid Sedan 2010       3435         17      0.899      0.882      0.964      0.916
Chevrolet TrailBlazer SS 2009       3435         16      0.998          1      0.995      0.936
Chevrolet Silverado 2500HD Regular Cab 2012       3435         16      0.698      0.875      0.851      0.767
Chevrolet Silverado 1500 Classic Extended Cab 2007       3435         18      0.923          1      0.983      0.927
Chevrolet Express Van 2007       3435         14          1      0.218      0.624      0.606
Chevrolet Monte Carlo Coupe 2007       3435         19          1      0.928      0.995      0.916
Chevrolet Malibu Sedan 2007       3435         19      0.912      0.789      0.909      0.831
Chevrolet Silverado 1500 Extended Cab 2012       3435         19      0.628      0.737      0.755      0.702
Chevrolet Silverado 1500 Regular Cab 2012       3435         19      0.639      0.839      0.813      0.779
Chrysler Aspen SUV 2009       3435         19      0.781      0.895      0.956      0.907
Chrysler Sebring Convertible 2010       3435         17      0.971      0.941       0.99      0.943
Chrysler Town and Country Minivan 2012       3435         16      0.881          1      0.991      0.911
Chrysler 300 SRT-8 2010       3435         21      0.937          1      0.982      0.914
Chrysler Crossfire Convertible 2008       3435         18      0.968          1      0.995      0.963
Chrysler PT Cruiser Convertible 2008       3435         19      0.939          1      0.995      0.892
Daewoo Nubira Wagon 2002       3435         19      0.901          1       0.99      0.916
Dodge Caliber Wagon 2012       3435         17      0.912      0.765      0.895      0.841
Dodge Caliber Wagon 2007       3435         18      0.763          1      0.839      0.803
Dodge Caravan Minivan 1997       3435         19      0.974          1      0.995      0.947
Dodge Ram Pickup 3500 Crew Cab 2010       3435         18          1      0.985      0.995      0.935
Dodge Ram Pickup 3500 Quad Cab 2009       3435         19      0.949      0.982       0.99      0.944
Dodge Sprinter Cargo Van 2009       3435         17      0.829       0.57        0.9      0.795
Dodge Journey SUV 2012       3435         19      0.946          1      0.995      0.958
Dodge Dakota Crew Cab 2010       3435         17      0.867      0.941      0.985      0.942
Dodge Dakota Club Cab 2007       3435         17      0.893      0.984      0.976      0.946
Dodge Magnum Wagon 2008       3435         16      0.941      0.938      0.961      0.882
Dodge Challenger SRT8 2011       3435         17      0.955          1      0.995      0.957
Dodge Durango SUV 2012       3435         19      0.966          1      0.995      0.947
Dodge Durango SUV 2007       3435         19      0.896      0.905      0.969      0.863
Dodge Charger Sedan 2012       3435         17      0.901      0.882      0.936      0.919
Dodge Charger SRT-8 2009       3435         18      0.927      0.944      0.981      0.925
Eagle Talon Hatchback 1998       3435         19       0.99          1      0.995      0.925
  FIAT 500 Abarth 2012       3435         12      0.948          1      0.995       0.89
FIAT 500 Convertible 2012       3435         15      0.967          1      0.995      0.952
 Ferrari FF Coupe 2012       3435         18      0.967          1      0.995      0.926
Ferrari California Convertible 2012       3435         17      0.961          1      0.995      0.932
Ferrari 458 Italia Convertible 2012       3435         17      0.824      0.941      0.963      0.845
Ferrari 458 Italia Coupe 2012       3435         18      0.848      0.944      0.943      0.875
Fisker Karma Sedan 2012       3435         19       0.89          1      0.995      0.958
Ford F-450 Super Duty Crew Cab 2012       3435         17          1      0.998      0.995      0.963
Ford Mustang Convertible 2007       3435         19          1      0.969      0.995      0.871
Ford Freestar Minivan 2007       3435         19      0.969          1      0.995      0.928
Ford Expedition EL SUV 2009       3435         19       0.99      0.947      0.976      0.918
    Ford Edge SUV 2012       3435         18      0.959          1      0.995      0.882
Ford Ranger SuperCab 2011       3435         18      0.966          1      0.995       0.97
    Ford GT Coupe 2006       3435         19      0.875          1      0.971      0.872
Ford F-150 Regular Cab 2012       3435         18        0.9          1      0.995      0.954
Ford F-150 Regular Cab 2007       3435         19      0.973      0.947      0.993      0.956
 Ford Focus Sedan 2007       3435         19      0.912      0.947      0.979      0.883
Ford E-Series Wagon Van 2012       3435         16      0.964          1      0.995      0.944
Ford Fiesta Sedan 2012       3435         18      0.888          1      0.995      0.948
  GMC Terrain SUV 2012       3435         17      0.969          1      0.995       0.94
   GMC Savana Van 2012       3435         28      0.757      0.929      0.942      0.898
GMC Yukon Hybrid SUV 2012       3435         18       0.83      0.944      0.969      0.944
   GMC Acadia SUV 2012       3435         19      0.898          1       0.99      0.864
GMC Canyon Extended Cab 2012       3435         16      0.914          1      0.984      0.915
Geo Metro Convertible 1993       3435         19      0.944      0.842      0.978      0.885
HUMMER H3T Crew Cab 2010       3435         17      0.811          1      0.924      0.829
HUMMER H2 SUT Crew Cab 2009       3435         19      0.892      0.869      0.937       0.88
Honda Odyssey Minivan 2012       3435         18      0.925          1      0.995      0.972
Honda Odyssey Minivan 2007       3435         17      0.981          1      0.995      0.955
Honda Accord Coupe 2012       3435         17      0.912      0.882      0.972      0.938
Honda Accord Sedan 2012       3435         17      0.886          1      0.992      0.951
Hyundai Veloster Hatchback 2012       3435         17      0.961          1      0.995      0.901
Hyundai Santa Fe SUV 2012       3435         18      0.976          1      0.995      0.925
Hyundai Tucson SUV 2012       3435         19      0.927          1       0.99      0.955
Hyundai Veracruz SUV 2012       3435         18      0.887      0.944      0.972      0.934
Hyundai Sonata Hybrid Sedan 2012       3435         15      0.986          1      0.995      0.964
Hyundai Elantra Sedan 2007       3435         18      0.991          1      0.995      0.976
Hyundai Accent Sedan 2012       3435         11          1      0.962      0.995       0.95
Hyundai Genesis Sedan 2012       3435         19      0.846          1      0.995      0.932
Hyundai Sonata Sedan 2012       3435         17      0.957      0.941       0.99      0.877
Hyundai Elantra Touring Hatchback 2012       3435         18      0.922          1      0.995       0.92
Hyundai Azera Sedan 2012       3435         18      0.853      0.833      0.826      0.785
Infiniti G Coupe IPL 2012       3435         15      0.962          1      0.995      0.952
Infiniti QX56 SUV 2011       3435         14      0.964          1      0.995      0.973
Isuzu Ascender SUV 2008       3435         16      0.989          1      0.995      0.905
    Jaguar XK XKR 2012       3435         19      0.958      0.895      0.982      0.791
 Jeep Patriot SUV 2012       3435         19      0.965          1      0.995      0.971
Jeep Wrangler SUV 2012       3435         18      0.948          1      0.995      0.905
 Jeep Liberty SUV 2012       3435         19      0.963          1      0.995      0.969
Jeep Grand Cherokee SUV 2012       3435         19      0.966      0.895      0.972      0.922
 Jeep Compass SUV 2012       3435         18      0.868          1      0.974      0.952
Lamborghini Reventon Coupe 2008       3435         15      0.973      0.933      0.988      0.864
Lamborghini Aventador Coupe 2012       3435         19      0.853      0.947      0.954       0.83
Lamborghini Gallardo LP 570-4 Superleggera 2012       3435         15      0.952      0.933      0.982       0.89
Lamborghini Diablo Coupe 2001       3435         19       0.92      0.947      0.953      0.842
Land Rover Range Rover SUV 2012       3435         18      0.928          1      0.995      0.981
Land Rover LR2 SUV 2012       3435         18      0.936          1      0.992      0.967
Lincoln Town Car Sedan 2011       3435         17      0.959      0.941      0.992      0.866
MINI Cooper Roadster Convertible 2012       3435         15      0.974          1      0.995      0.931
Maybach Landaulet Convertible 2012       3435         13      0.891          1      0.984      0.916
Mazda Tribute SUV 2011       3435         15      0.935      0.966      0.991      0.973
McLaren MP4-12C Coupe 2012       3435         19      0.941          1      0.995      0.963
Mercedes-Benz 300-Class Convertible 1993       3435         20      0.955       0.95      0.983      0.821
Mercedes-Benz C-Class Sedan 2012       3435         19      0.926      0.947      0.988      0.828
Mercedes-Benz SL-Class Coupe 2009       3435         15      0.963          1      0.995      0.934
Mercedes-Benz E-Class Sedan 2012       3435         19      0.939          1      0.995      0.966
Mercedes-Benz S-Class Sedan 2012       3435         19       0.94          1      0.995      0.982
Mercedes-Benz Sprinter Van 2012       3435         17      0.607      0.941      0.906       0.86
Mitsubishi Lancer Sedan 2012       3435         20      0.911       0.95      0.967      0.887
Nissan Leaf Hatchback 2012       3435         18      0.895      0.948       0.99      0.906
Nissan NV Passenger Van 2012       3435         17      0.919          1      0.995      0.848
Nissan Juke Hatchback 2012       3435         19      0.902      0.973      0.988      0.932
Nissan 240SX Coupe 1998       3435         19      0.909          1      0.995       0.93
Plymouth Neon Coupe 1999       3435         19      0.965          1      0.995      0.964
Porsche Panamera Sedan 2012       3435         19          1      0.987      0.995      0.974
Ram C-V Cargo Van Minivan 2012       3435         17       0.93      0.882      0.966      0.851
Rolls-Royce Phantom Drophead Coupe Convertible 2012       3435         13      0.966          1      0.995      0.942
Rolls-Royce Ghost Sedan 2012       3435         17      0.805      0.973      0.947      0.917
Rolls-Royce Phantom Sedan 2012       3435         19      0.942      0.862      0.951       0.81
Scion xD Hatchback 2012       3435         17      0.866          1      0.995       0.95
Spyker C8 Convertible 2009       3435         19      0.783      0.842      0.777      0.649
  Spyker C8 Coupe 2009       3435         18       0.87      0.944      0.933      0.857
Suzuki Aerio Sedan 2007       3435         16      0.839       0.98      0.986      0.941
Suzuki Kizashi Sedan 2012       3435         19          1      0.889      0.947       0.86
Suzuki SX4 Hatchback 2012       3435         18      0.963          1      0.995      0.973
 Suzuki SX4 Sedan 2012       3435         17       0.96          1      0.995      0.957
Tesla Model S Sedan 2012       3435         17      0.955          1      0.995      0.893
Toyota Sequoia SUV 2012       3435         17          1      0.873      0.995      0.948
Toyota Camry Sedan 2012       3435         19      0.855      0.928      0.904      0.889
Toyota Corolla Sedan 2012       3435         19      0.955      0.842      0.897      0.809
Toyota 4Runner SUV 2012       3435         17      0.925          1      0.995      0.946
Volkswagen Golf Hatchback 2012       3435         18      0.973          1      0.995      0.946
Volkswagen Golf Hatchback 1991       3435         19       0.88          1      0.995      0.954
Volkswagen Beetle Hatchback 2012       3435         18      0.957      0.944      0.986      0.958
Volvo C30 Hatchback 2012       3435         17      0.868          1      0.995      0.956
  Volvo 240 Sedan 1993       3435         19      0.972          1      0.995      0.917
   Volvo XC90 SUV 2007       3435         18      0.961          1      0.995      0.977
smart fortwo Convertible 2012       3435         16      0.915          1      0.995      0.942
Results saved to yolov5/runs/train/exp

Model pickling

• Note that the model is automatically saved to the folder "yolov5/runs/train/exp" by yolov5 library.
• From here, we can download the 'yolov5/runs/train/exp/weights/best.pt' weights and reload them for future use.
• Hence, "pickling" the model is given out of box to us by yolo library.
• To check how we can reload the pretrained model, Please refer to the "Basic clickable UI" section at the end of this notebook.

Image(filename='/content/yolov5/runs/train/exp/results.png')

Evaluate on test images

!python yolov5/val.py --batch 64 --data cars_data.yaml --weights '/content/yolov5/runs/train/exp/weights/best.pt' --task test --img-size 320

val: data=cars_data.yaml, weights=['/content/yolov5/runs/train/exp/weights/best.pt'], batch_size=64, imgsz=320, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=yolov5/runs/val, name=exp, exist_ok=False, half=False, dnn=False
YOLOv5 🚀 v6.2-263-g0307954 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
test: Scanning /content/data/labels/test... 1524 images, 0 backgrounds, 0 corrupt: 100% 1524/1524 [00:04<00:00, 376.31it/s]
test: New cache created: /content/data/labels/test.cache
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:21<00:00,  1.12it/s]
                   all       1524       1524      0.877       0.93      0.956      0.902
Speed: 0.1ms pre-process, 1.6ms inference, 2.1ms NMS per image at shape (64, 3, 320, 320)
Results saved to yolov5/runs/val/exp

Predict all the test images

!python yolov5/detect.py --source '/content/data/images/test' --weights '/content/yolov5/runs/train/exp/weights/best.pt' --img-size 320

detect: weights=['/content/yolov5/runs/train/exp/weights/best.pt'], source=/content/data/images/test, data=yolov5/data/coco128.yaml, imgsz=[320, 320], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v6.2-263-g0307954 Python-3.7.15 torch-1.13.0+cu117 CUDA:0 (Tesla T4, 15110MiB)

Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
image 1/1524 /content/data/images/test/test_00018.jpg: 224x320 1 BMW X6 SUV 2012, 10.9ms
image 2/1524 /content/data/images/test/test_00044.jpg: 256x320 1 Chevrolet Avalanche Crew Cab 2012, 12.1ms
image 3/1524 /content/data/images/test/test_00047.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.5ms
image 4/1524 /content/data/images/test/test_00048.jpg: 256x320 1 Dodge Caravan Minivan 1997, 8.4ms
image 5/1524 /content/data/images/test/test_00050.jpg: 224x320 1 Nissan Leaf Hatchback 2012, 9.3ms
image 6/1524 /content/data/images/test/test_00054.jpg: 256x320 1 Lincoln Town Car Sedan 2011, 7.8ms
image 7/1524 /content/data/images/test/test_00056.jpg: 224x320 1 GMC Yukon Hybrid SUV 2012, 8.6ms
image 8/1524 /content/data/images/test/test_00075.jpg: 256x320 1 Nissan Leaf Hatchback 2012, 7.6ms
image 9/1524 /content/data/images/test/test_00079.jpg: 192x320 1 Chevrolet Sonic Sedan 2012, 10.7ms
image 10/1524 /content/data/images/test/test_00083.jpg: 224x320 1 Fisker Karma Sedan 2012, 7.7ms
image 11/1524 /content/data/images/test/test_00090.jpg: 256x320 1 Dodge Challenger SRT8 2011, 7.6ms
image 12/1524 /content/data/images/test/test_00101.jpg: 256x320 1 Toyota 4Runner SUV 2012, 7.8ms
image 13/1524 /content/data/images/test/test_00104.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 7.1ms
image 14/1524 /content/data/images/test/test_00128.jpg: 224x320 1 Acura Integra Type R 2001, 7.2ms
image 15/1524 /content/data/images/test/test_00131.jpg: 256x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 1 Dodge Ram Pickup 3500 Quad Cab 2009, 7.3ms
image 16/1524 /content/data/images/test/test_00135.jpg: 224x320 1 BMW 6 Series Convertible 2007, 1 BMW M6 Convertible 2010, 7.5ms
image 17/1524 /content/data/images/test/test_00139.jpg: 192x320 1 Hyundai Veracruz SUV 2012, 7.2ms
image 18/1524 /content/data/images/test/test_00141.jpg: 288x320 1 Hyundai Sonata Sedan 2012, 11.0ms
image 19/1524 /content/data/images/test/test_00145.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 7.5ms
image 20/1524 /content/data/images/test/test_00147.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 8.9ms
image 21/1524 /content/data/images/test/test_00148.jpg: 192x320 1 BMW 1 Series Coupe 2012, 1 BMW M3 Coupe 2012, 7.5ms
image 22/1524 /content/data/images/test/test_00149.jpg: 256x320 1 Audi TT Hatchback 2011, 7.6ms
image 23/1524 /content/data/images/test/test_00158.jpg: 224x320 1 Aston Martin Virage Convertible 2012, 7.6ms
image 24/1524 /content/data/images/test/test_00162.jpg: 192x320 1 FIAT 500 Abarth 2012, 7.3ms
image 25/1524 /content/data/images/test/test_00167.jpg: 256x320 1 BMW X3 SUV 2012, 7.7ms
image 26/1524 /content/data/images/test/test_00178.jpg: 288x320 1 Dodge Sprinter Cargo Van 2009, 7.9ms
image 27/1524 /content/data/images/test/test_00182.jpg: 224x320 1 Bentley Mulsanne Sedan 2011, 7.6ms
image 28/1524 /content/data/images/test/test_00186.jpg: 256x320 1 Audi S5 Coupe 2012, 8.0ms
image 29/1524 /content/data/images/test/test_00198.jpg: 160x320 1 BMW 1 Series Coupe 2012, 12.5ms
image 30/1524 /content/data/images/test/test_00210.jpg: 224x320 1 Audi S4 Sedan 2007, 7.6ms
image 31/1524 /content/data/images/test/test_00219.jpg: 160x320 1 Ford Freestar Minivan 2007, 10.6ms
image 32/1524 /content/data/images/test/test_00222.jpg: 256x320 1 Volvo C30 Hatchback 2012, 7.6ms
image 33/1524 /content/data/images/test/test_00224.jpg: 256x320 1 Buick Enclave SUV 2012, 7.4ms
image 34/1524 /content/data/images/test/test_00225.jpg: 192x320 1 Jeep Compass SUV 2012, 7.4ms
image 35/1524 /content/data/images/test/test_00228.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 7.5ms
image 36/1524 /content/data/images/test/test_00240.jpg: 224x320 1 Lamborghini Diablo Coupe 2001, 7.6ms
image 37/1524 /content/data/images/test/test_00242.jpg: 224x320 1 Geo Metro Convertible 1993, 8.0ms
image 38/1524 /content/data/images/test/test_00246.jpg: 256x320 1 Chevrolet Malibu Hybrid Sedan 2010, 7.9ms
image 39/1524 /content/data/images/test/test_00250.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 7.6ms
image 40/1524 /content/data/images/test/test_00252.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 12.5ms
image 41/1524 /content/data/images/test/test_00256.jpg: 224x320 1 Dodge Durango SUV 2007, 7.8ms
image 42/1524 /content/data/images/test/test_00257.jpg: 224x320 1 Hyundai Veloster Hatchback 2012, 8.1ms
image 43/1524 /content/data/images/test/test_00272.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 8.3ms
image 44/1524 /content/data/images/test/test_00273.jpg: 256x320 (no detections), 7.6ms
image 45/1524 /content/data/images/test/test_00275.jpg: 256x320 1 Nissan 240SX Coupe 1998, 9.4ms
image 46/1524 /content/data/images/test/test_00279.jpg: 256x320 1 Toyota 4Runner SUV 2012, 7.7ms
image 47/1524 /content/data/images/test/test_00281.jpg: 288x320 1 Ford Expedition EL SUV 2009, 8.7ms
image 48/1524 /content/data/images/test/test_00283.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 7.8ms
image 49/1524 /content/data/images/test/test_00292.jpg: 256x320 (no detections), 7.5ms
image 50/1524 /content/data/images/test/test_00304.jpg: 256x320 1 Jeep Wrangler SUV 2012, 7.2ms
image 51/1524 /content/data/images/test/test_00315.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 7.0ms
image 52/1524 /content/data/images/test/test_00316.jpg: 224x320 1 Tesla Model S Sedan 2012, 7.6ms
image 53/1524 /content/data/images/test/test_00320.jpg: 256x320 1 Eagle Talon Hatchback 1998, 7.6ms
image 54/1524 /content/data/images/test/test_00336.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 7.4ms
image 55/1524 /content/data/images/test/test_00340.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 7.4ms
image 56/1524 /content/data/images/test/test_00341.jpg: 256x320 1 Spyker C8 Convertible 2009, 7.5ms
image 57/1524 /content/data/images/test/test_00345.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 8.1ms
image 58/1524 /content/data/images/test/test_00347.jpg: 128x320 1 Acura RL Sedan 2012, 1 Hyundai Genesis Sedan 2012, 10.4ms
image 59/1524 /content/data/images/test/test_00353.jpg: 256x320 (no detections), 7.7ms
image 60/1524 /content/data/images/test/test_00355.jpg: 224x320 1 Chrysler Town and Country Minivan 2012, 8.1ms
image 61/1524 /content/data/images/test/test_00361.jpg: 256x320 1 BMW 6 Series Convertible 2007, 7.6ms
image 62/1524 /content/data/images/test/test_00364.jpg: 256x320 1 Nissan 240SX Coupe 1998, 8.0ms
image 63/1524 /content/data/images/test/test_00374.jpg: 256x320 1 Suzuki SX4 Hatchback 2012, 7.4ms
image 64/1524 /content/data/images/test/test_00388.jpg: 224x320 1 Land Rover LR2 SUV 2012, 7.9ms
image 65/1524 /content/data/images/test/test_00389.jpg: 224x320 1 Audi R8 Coupe 2012, 7.8ms
image 66/1524 /content/data/images/test/test_00390.jpg: 192x320 1 Dodge Caliber Wagon 2007, 7.6ms
image 67/1524 /content/data/images/test/test_00397.jpg: 224x320 1 Acura TL Sedan 2012, 7.8ms
image 68/1524 /content/data/images/test/test_00400.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 7.2ms
image 69/1524 /content/data/images/test/test_00403.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 7.5ms
image 70/1524 /content/data/images/test/test_00404.jpg: 256x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 7.3ms
image 71/1524 /content/data/images/test/test_00409.jpg: 224x320 1 Porsche Panamera Sedan 2012, 7.9ms
image 72/1524 /content/data/images/test/test_00412.jpg: 256x320 1 Audi S6 Sedan 2011, 7.9ms
image 73/1524 /content/data/images/test/test_00415.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 7.6ms
image 74/1524 /content/data/images/test/test_00431.jpg: 224x320 1 Rolls-Royce Phantom Sedan 2012, 7.9ms
image 75/1524 /content/data/images/test/test_00433.jpg: 192x320 1 Audi S6 Sedan 2011, 7.7ms
image 76/1524 /content/data/images/test/test_00440.jpg: 256x320 1 Ford Edge SUV 2012, 7.9ms
image 77/1524 /content/data/images/test/test_00445.jpg: 192x320 1 Ford F-150 Regular Cab 2007, 8.6ms
image 78/1524 /content/data/images/test/test_00455.jpg: 224x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 7.8ms
image 79/1524 /content/data/images/test/test_00467.jpg: 192x320 1 Aston Martin V8 Vantage Coupe 2012, 9.0ms
image 80/1524 /content/data/images/test/test_00468.jpg: 320x320 2 Maybach Landaulet Convertible 2012s, 8.0ms
image 81/1524 /content/data/images/test/test_00483.jpg: 224x320 1 Volvo C30 Hatchback 2012, 8.6ms
image 82/1524 /content/data/images/test/test_00486.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 8.7ms
image 83/1524 /content/data/images/test/test_00494.jpg: 224x320 1 Chevrolet Monte Carlo Coupe 2007, 7.7ms
image 84/1524 /content/data/images/test/test_00500.jpg: 224x320 1 Spyker C8 Convertible 2009, 7.4ms
image 85/1524 /content/data/images/test/test_00504.jpg: 224x320 1 BMW X6 SUV 2012, 8.0ms
image 86/1524 /content/data/images/test/test_00518.jpg: 256x320 1 BMW M6 Convertible 2010, 7.7ms
image 87/1524 /content/data/images/test/test_00528.jpg: 224x320 1 Ferrari 458 Italia Convertible 2012, 1 Lamborghini Aventador Coupe 2012, 7.7ms
image 88/1524 /content/data/images/test/test_00531.jpg: 256x320 1 Ford Freestar Minivan 2007, 8.6ms
image 89/1524 /content/data/images/test/test_00532.jpg: 320x224 1 Aston Martin V8 Vantage Convertible 2012, 11.3ms
image 90/1524 /content/data/images/test/test_00535.jpg: 192x320 1 Jeep Patriot SUV 2012, 7.4ms
image 91/1524 /content/data/images/test/test_00538.jpg: 192x320 1 Lamborghini Reventon Coupe 2008, 7.1ms
image 92/1524 /content/data/images/test/test_00543.jpg: 256x320 1 Audi S6 Sedan 2011, 7.7ms
image 93/1524 /content/data/images/test/test_00544.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 7.7ms
image 94/1524 /content/data/images/test/test_00546.jpg: 224x320 1 Ford Focus Sedan 2007, 1 Suzuki Aerio Sedan 2007, 7.6ms
image 95/1524 /content/data/images/test/test_00557.jpg: 224x320 1 Aston Martin V8 Vantage Coupe 2012, 7.2ms
image 96/1524 /content/data/images/test/test_00563.jpg: 256x320 1 Volvo C30 Hatchback 2012, 7.7ms
image 97/1524 /content/data/images/test/test_00566.jpg: 192x320 1 Chrysler 300 SRT-8 2010, 8.2ms
image 98/1524 /content/data/images/test/test_00568.jpg: 160x320 1 HUMMER H3T Crew Cab 2010, 7.6ms
image 99/1524 /content/data/images/test/test_00573.jpg: 256x320 1 Audi 100 Sedan 1994, 7.9ms
image 100/1524 /content/data/images/test/test_00582.jpg: 160x320 1 Infiniti G Coupe IPL 2012, 7.5ms
image 101/1524 /content/data/images/test/test_00591.jpg: 256x320 1 BMW 3 Series Wagon 2012, 7.7ms
image 102/1524 /content/data/images/test/test_00603.jpg: 256x320 1 Audi TT RS Coupe 2012, 7.5ms
image 103/1524 /content/data/images/test/test_00608.jpg: 160x320 1 Honda Odyssey Minivan 2007, 11.4ms
image 104/1524 /content/data/images/test/test_00611.jpg: 256x320 1 Jeep Patriot SUV 2012, 10.8ms
image 105/1524 /content/data/images/test/test_00614.jpg: 224x320 1 Hyundai Azera Sedan 2012, 7.8ms
image 106/1524 /content/data/images/test/test_00623.jpg: 256x320 1 Dodge Caliber Wagon 2007, 7.6ms
image 107/1524 /content/data/images/test/test_00626.jpg: 224x320 1 Toyota Camry Sedan 2012, 7.4ms
image 108/1524 /content/data/images/test/test_00638.jpg: 224x320 1 BMW M3 Coupe 2012, 7.3ms
image 109/1524 /content/data/images/test/test_00639.jpg: 224x320 1 Jaguar XK XKR 2012, 1 Porsche Panamera Sedan 2012, 7.2ms
image 110/1524 /content/data/images/test/test_00641.jpg: 256x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 7.9ms
image 111/1524 /content/data/images/test/test_00646.jpg: 224x320 1 BMW Z4 Convertible 2012, 7.5ms
image 112/1524 /content/data/images/test/test_00647.jpg: 256x320 1 Ford Edge SUV 2012, 7.6ms
image 113/1524 /content/data/images/test/test_00651.jpg: 224x320 1 Ford Expedition EL SUV 2009, 8.4ms
image 114/1524 /content/data/images/test/test_00654.jpg: 256x320 1 Ford Freestar Minivan 2007, 8.6ms
image 115/1524 /content/data/images/test/test_00666.jpg: 224x320 1 Ferrari California Convertible 2012, 7.9ms
image 116/1524 /content/data/images/test/test_00668.jpg: 224x320 1 Mercedes-Benz Sprinter Van 2012, 7.5ms
image 117/1524 /content/data/images/test/test_00670.jpg: 256x320 1 GMC Savana Van 2012, 8.1ms
image 118/1524 /content/data/images/test/test_00683.jpg: 192x320 1 Toyota 4Runner SUV 2012, 8.3ms
image 119/1524 /content/data/images/test/test_00684.jpg: 192x320 1 AM General Hummer SUV 2000, 7.3ms
image 120/1524 /content/data/images/test/test_00685.jpg: 224x320 1 Jaguar XK XKR 2012, 7.7ms
image 121/1524 /content/data/images/test/test_00687.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 7.7ms
image 122/1524 /content/data/images/test/test_00688.jpg: 256x320 1 Audi TTS Coupe 2012, 1 Audi TT Hatchback 2011, 8.6ms
image 123/1524 /content/data/images/test/test_00689.jpg: 224x320 1 Ferrari California Convertible 2012, 7.6ms
image 124/1524 /content/data/images/test/test_00697.jpg: 256x320 1 Dodge Charger Sedan 2012, 8.1ms
image 125/1524 /content/data/images/test/test_00698.jpg: 224x320 1 Dodge Caliber Wagon 2007, 7.6ms
image 126/1524 /content/data/images/test/test_00706.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 7.7ms
image 127/1524 /content/data/images/test/test_00709.jpg: 224x320 1 Audi S5 Convertible 2012, 7.5ms
image 128/1524 /content/data/images/test/test_00713.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 7.5ms
image 129/1524 /content/data/images/test/test_00714.jpg: 256x320 1 Honda Odyssey Minivan 2012, 7.9ms
image 130/1524 /content/data/images/test/test_00725.jpg: 192x320 1 MINI Cooper Roadster Convertible 2012, 7.3ms
image 131/1524 /content/data/images/test/test_00734.jpg: 256x320 1 Ford Freestar Minivan 2007, 7.6ms
image 132/1524 /content/data/images/test/test_00746.jpg: 192x320 1 Bentley Continental GT Coupe 2012, 1 Bentley Continental GT Coupe 2007, 10.0ms
image 133/1524 /content/data/images/test/test_00759.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 7.5ms
image 134/1524 /content/data/images/test/test_00760.jpg: 192x320 (no detections), 7.6ms
image 135/1524 /content/data/images/test/test_00766.jpg: 256x320 1 Volkswagen Golf Hatchback 2012, 7.8ms
image 136/1524 /content/data/images/test/test_00777.jpg: 224x320 1 Aston Martin Virage Coupe 2012, 7.5ms
image 137/1524 /content/data/images/test/test_00778.jpg: 256x320 1 Audi TTS Coupe 2012, 8.1ms
image 138/1524 /content/data/images/test/test_00779.jpg: 256x320 1 Jeep Wrangler SUV 2012, 7.2ms
image 139/1524 /content/data/images/test/test_00781.jpg: 256x320 1 Chevrolet Tahoe Hybrid SUV 2012, 7.4ms
image 140/1524 /content/data/images/test/test_00784.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 7.4ms
image 141/1524 /content/data/images/test/test_00787.jpg: 224x320 1 Toyota Corolla Sedan 2012, 10.1ms
image 142/1524 /content/data/images/test/test_00796.jpg: 224x320 1 BMW 3 Series Sedan 2012, 7.5ms
image 143/1524 /content/data/images/test/test_00797.jpg: 224x320 1 Toyota Sequoia SUV 2012, 7.3ms
image 144/1524 /content/data/images/test/test_00804.jpg: 224x320 1 Lamborghini Aventador Coupe 2012, 7.3ms
image 145/1524 /content/data/images/test/test_00806.jpg: 256x320 1 Audi TT Hatchback 2011, 1 Audi TT RS Coupe 2012, 7.7ms
image 146/1524 /content/data/images/test/test_00807.jpg: 256x320 1 Mercedes-Benz SL-Class Coupe 2009, 7.4ms
image 147/1524 /content/data/images/test/test_00808.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 7.8ms
image 148/1524 /content/data/images/test/test_00809.jpg: 224x320 1 Aston Martin Virage Coupe 2012, 7.4ms
image 149/1524 /content/data/images/test/test_00818.jpg: 256x320 1 BMW M5 Sedan 2010, 9.9ms
image 150/1524 /content/data/images/test/test_00819.jpg: 256x320 1 Ford Fiesta Sedan 2012, 7.4ms
image 151/1524 /content/data/images/test/test_00833.jpg: 256x320 1 Mercedes-Benz E-Class Sedan 2012, 11.9ms
image 152/1524 /content/data/images/test/test_00835.jpg: 256x320 1 Acura TSX Sedan 2012, 7.3ms
image 153/1524 /content/data/images/test/test_00838.jpg: 256x320 1 Mercedes-Benz S-Class Sedan 2012, 7.4ms
image 154/1524 /content/data/images/test/test_00841.jpg: 256x320 1 BMW 6 Series Convertible 2007, 7.8ms
image 155/1524 /content/data/images/test/test_00842.jpg: 256x320 1 Plymouth Neon Coupe 1999, 8.5ms
image 156/1524 /content/data/images/test/test_00844.jpg: 256x320 1 Scion xD Hatchback 2012, 7.6ms
image 157/1524 /content/data/images/test/test_00845.jpg: 192x320 1 Ferrari 458 Italia Convertible 2012, 1 Ferrari 458 Italia Coupe 2012, 7.8ms
image 158/1524 /content/data/images/test/test_00850.jpg: 256x320 1 Chevrolet Cobalt SS 2010, 8.4ms
image 159/1524 /content/data/images/test/test_00851.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 7.9ms
image 160/1524 /content/data/images/test/test_00852.jpg: 192x320 1 Rolls-Royce Ghost Sedan 2012, 7.3ms
image 161/1524 /content/data/images/test/test_00854.jpg: 192x320 1 Jeep Liberty SUV 2012, 7.3ms
image 162/1524 /content/data/images/test/test_00858.jpg: 256x320 1 Ford Ranger SuperCab 2011, 8.1ms
image 163/1524 /content/data/images/test/test_00861.jpg: 256x320 1 Nissan 240SX Coupe 1998, 7.6ms
image 164/1524 /content/data/images/test/test_00863.jpg: 256x320 1 Cadillac SRX SUV 2012, 7.5ms
image 165/1524 /content/data/images/test/test_00870.jpg: 160x320 1 Volvo C30 Hatchback 2012, 7.5ms
image 166/1524 /content/data/images/test/test_00889.jpg: 224x320 1 MINI Cooper Roadster Convertible 2012, 7.6ms
image 167/1524 /content/data/images/test/test_00894.jpg: 224x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 7.4ms
image 168/1524 /content/data/images/test/test_00897.jpg: 224x320 1 Jaguar XK XKR 2012, 7.3ms
image 169/1524 /content/data/images/test/test_00899.jpg: 224x320 1 Audi RS 4 Convertible 2008, 7.7ms
image 170/1524 /content/data/images/test/test_00913.jpg: 224x320 1 Acura RL Sedan 2012, 7.4ms
image 171/1524 /content/data/images/test/test_00925.jpg: 256x320 1 Dodge Caravan Minivan 1997, 7.8ms
image 172/1524 /content/data/images/test/test_00928.jpg: 256x320 1 Audi S6 Sedan 2011, 1 Audi S4 Sedan 2012, 8.1ms
image 173/1524 /content/data/images/test/test_00933.jpg: 256x320 1 smart fortwo Convertible 2012, 9.4ms
image 174/1524 /content/data/images/test/test_00954.jpg: 160x320 1 Audi 100 Sedan 1994, 1 Audi 100 Wagon 1994, 7.9ms
image 175/1524 /content/data/images/test/test_00955.jpg: 224x320 1 Chevrolet Cobalt SS 2010, 8.1ms
image 176/1524 /content/data/images/test/test_00966.jpg: 256x320 1 Honda Odyssey Minivan 2007, 7.6ms
image 177/1524 /content/data/images/test/test_00974.jpg: 256x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 7.5ms
image 178/1524 /content/data/images/test/test_00977.jpg: 192x320 1 Volkswagen Golf Hatchback 2012, 7.9ms
image 179/1524 /content/data/images/test/test_00983.jpg: 224x320 1 Dodge Journey SUV 2012, 8.1ms
image 180/1524 /content/data/images/test/test_00994.jpg: 256x320 1 Dodge Caliber Wagon 2007, 8.0ms
image 181/1524 /content/data/images/test/test_00995.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 7.6ms
image 182/1524 /content/data/images/test/test_01004.jpg: 256x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 7.3ms
image 183/1524 /content/data/images/test/test_01010.jpg: 256x320 1 Audi S6 Sedan 2011, 7.7ms
image 184/1524 /content/data/images/test/test_01012.jpg: 256x320 1 Audi 100 Sedan 1994, 1 Audi 100 Wagon 1994, 7.4ms
image 185/1524 /content/data/images/test/test_01014.jpg: 256x320 1 Toyota Corolla Sedan 2012, 9.7ms
image 186/1524 /content/data/images/test/test_01015.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 7.7ms
image 187/1524 /content/data/images/test/test_01016.jpg: 256x320 1 Dodge Magnum Wagon 2008, 7.8ms
image 188/1524 /content/data/images/test/test_01020.jpg: 192x320 1 BMW Z4 Convertible 2012, 8.0ms
image 189/1524 /content/data/images/test/test_01022.jpg: 224x320 1 Acura TL Sedan 2012, 8.0ms
image 190/1524 /content/data/images/test/test_01024.jpg: 160x320 1 HUMMER H2 SUT Crew Cab 2009, 8.1ms
image 191/1524 /content/data/images/test/test_01028.jpg: 224x320 1 GMC Acadia SUV 2012, 8.6ms
image 192/1524 /content/data/images/test/test_01036.jpg: 256x320 1 Bugatti Veyron 16.4 Coupe 2009, 8.5ms
image 193/1524 /content/data/images/test/test_01041.jpg: 224x320 1 Audi S6 Sedan 2011, 8.4ms
image 194/1524 /content/data/images/test/test_01044.jpg: 256x320 1 GMC Acadia SUV 2012, 7.7ms
image 195/1524 /content/data/images/test/test_01048.jpg: 224x320 1 FIAT 500 Abarth 2012, 7.7ms
image 196/1524 /content/data/images/test/test_01059.jpg: 224x320 1 Bentley Continental Supersports Conv. Convertible 2012, 8.5ms
image 197/1524 /content/data/images/test/test_01060.jpg: 192x320 1 Chevrolet Corvette Ron Fellows Edition Z06 2007, 7.1ms
image 198/1524 /content/data/images/test/test_01066.jpg: 192x320 1 BMW 1 Series Convertible 2012, 6.9ms
image 199/1524 /content/data/images/test/test_01070.jpg: 256x320 1 Ford GT Coupe 2006, 7.3ms
image 200/1524 /content/data/images/test/test_01079.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 9.1ms
image 201/1524 /content/data/images/test/test_01083.jpg: 256x320 1 Audi 100 Wagon 1994, 7.5ms
image 202/1524 /content/data/images/test/test_01086.jpg: 256x320 1 Ferrari 458 Italia Convertible 2012, 7.6ms
image 203/1524 /content/data/images/test/test_01087.jpg: 192x320 1 Ferrari 458 Italia Convertible 2012, 7.2ms
image 204/1524 /content/data/images/test/test_01097.jpg: 224x320 1 Maybach Landaulet Convertible 2012, 7.9ms
image 205/1524 /content/data/images/test/test_01099.jpg: 256x320 1 Audi 100 Wagon 1994, 7.5ms
image 206/1524 /content/data/images/test/test_01103.jpg: 192x320 1 Buick Verano Sedan 2012, 7.3ms
image 207/1524 /content/data/images/test/test_01105.jpg: 224x320 1 Audi R8 Coupe 2012, 8.1ms
image 208/1524 /content/data/images/test/test_01117.jpg: 256x320 1 AM General Hummer SUV 2000, 7.9ms
image 209/1524 /content/data/images/test/test_01119.jpg: 320x320 1 FIAT 500 Convertible 2012, 7.6ms
image 210/1524 /content/data/images/test/test_01123.jpg: 256x320 1 Audi S4 Sedan 2012, 7.3ms
image 211/1524 /content/data/images/test/test_01126.jpg: 224x320 1 Infiniti G Coupe IPL 2012, 7.6ms
image 212/1524 /content/data/images/test/test_01127.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 7.1ms
image 213/1524 /content/data/images/test/test_01136.jpg: 224x320 1 Suzuki SX4 Sedan 2012, 6.9ms
image 214/1524 /content/data/images/test/test_01140.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 7.4ms
image 215/1524 /content/data/images/test/test_01142.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 8.9ms
image 216/1524 /content/data/images/test/test_01146.jpg: 192x320 1 Chevrolet HHR SS 2010, 7.6ms
image 217/1524 /content/data/images/test/test_01152.jpg: 224x320 1 BMW M5 Sedan 2010, 7.9ms
image 218/1524 /content/data/images/test/test_01153.jpg: 256x320 1 Dodge Caravan Minivan 1997, 7.6ms
image 219/1524 /content/data/images/test/test_01154.jpg: 224x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 8.1ms
image 220/1524 /content/data/images/test/test_01182.jpg: 256x320 1 Audi S5 Coupe 2012, 7.4ms
image 221/1524 /content/data/images/test/test_01183.jpg: 224x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 10.2ms
image 222/1524 /content/data/images/test/test_01190.jpg: 256x320 1 Isuzu Ascender SUV 2008, 8.0ms
image 223/1524 /content/data/images/test/test_01204.jpg: 224x320 1 Chevrolet Corvette Ron Fellows Edition Z06 2007, 7.3ms
image 224/1524 /content/data/images/test/test_01205.jpg: 192x320 1 Rolls-Royce Ghost Sedan 2012, 11.5ms
image 225/1524 /content/data/images/test/test_01210.jpg: 192x320 1 Bentley Continental GT Coupe 2007, 6.7ms
image 226/1524 /content/data/images/test/test_01221.jpg: 256x320 1 Dodge Dakota Crew Cab 2010, 7.5ms
image 227/1524 /content/data/images/test/test_01225.jpg: 224x320 1 BMW 3 Series Wagon 2012, 7.3ms
image 228/1524 /content/data/images/test/test_01230.jpg: 288x320 1 Toyota Sequoia SUV 2012, 7.9ms
image 229/1524 /content/data/images/test/test_01238.jpg: 256x320 1 smart fortwo Convertible 2012, 12.3ms
image 230/1524 /content/data/images/test/test_01256.jpg: 224x320 1 Chrysler PT Cruiser Convertible 2008, 8.6ms
image 231/1524 /content/data/images/test/test_01261.jpg: 224x320 1 BMW X5 SUV 2007, 7.4ms
image 232/1524 /content/data/images/test/test_01262.jpg: 192x320 1 Aston Martin Virage Coupe 2012, 7.3ms
image 233/1524 /content/data/images/test/test_01289.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 7.7ms
image 234/1524 /content/data/images/test/test_01291.jpg: 256x320 1 Acura ZDX Hatchback 2012, 7.6ms
image 235/1524 /content/data/images/test/test_01293.jpg: 224x320 1 smart fortwo Convertible 2012, 8.1ms
image 236/1524 /content/data/images/test/test_01294.jpg: 256x320 1 Mercedes-Benz SL-Class Coupe 2009, 8.4ms
image 237/1524 /content/data/images/test/test_01298.jpg: 224x320 1 BMW X6 SUV 2012, 9.5ms
image 238/1524 /content/data/images/test/test_01305.jpg: 256x320 1 Chrysler Aspen SUV 2009, 8.3ms
image 239/1524 /content/data/images/test/test_01307.jpg: 128x320 1 Buick Enclave SUV 2012, 7.6ms
image 240/1524 /content/data/images/test/test_01312.jpg: 256x320 1 Ford Mustang Convertible 2007, 7.9ms
image 241/1524 /content/data/images/test/test_01321.jpg: 224x320 1 Audi S5 Convertible 2012, 8.2ms
image 242/1524 /content/data/images/test/test_01326.jpg: 256x320 1 Acura Integra Type R 2001, 8.0ms
image 243/1524 /content/data/images/test/test_01329.jpg: 256x320 1 GMC Savana Van 2012, 8.0ms
image 244/1524 /content/data/images/test/test_01330.jpg: 224x320 1 Audi TT RS Coupe 2012, 7.6ms
image 245/1524 /content/data/images/test/test_01341.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 8.1ms
image 246/1524 /content/data/images/test/test_01344.jpg: 224x320 1 Jeep Wrangler SUV 2012, 7.8ms
image 247/1524 /content/data/images/test/test_01346.jpg: 256x320 1 Audi 100 Wagon 1994, 8.5ms
image 248/1524 /content/data/images/test/test_01350.jpg: 256x320 1 GMC Savana Van 2012, 8.0ms
image 249/1524 /content/data/images/test/test_01356.jpg: 256x320 1 Nissan NV Passenger Van 2012, 8.1ms
image 250/1524 /content/data/images/test/test_01365.jpg: 256x320 1 Dodge Sprinter Cargo Van 2009, 8.1ms
image 251/1524 /content/data/images/test/test_01368.jpg: 288x320 1 Bentley Continental Supersports Conv. Convertible 2012, 8.8ms
image 252/1524 /content/data/images/test/test_01371.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 13.6ms
image 253/1524 /content/data/images/test/test_01372.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 7.6ms
image 254/1524 /content/data/images/test/test_01376.jpg: 256x320 1 Hyundai Genesis Sedan 2012, 9.8ms
image 255/1524 /content/data/images/test/test_01379.jpg: 256x320 1 Hyundai Tucson SUV 2012, 8.0ms
image 256/1524 /content/data/images/test/test_01380.jpg: 256x320 1 Ferrari FF Coupe 2012, 8.1ms
image 257/1524 /content/data/images/test/test_01389.jpg: 224x320 1 Dodge Caliber Wagon 2007, 8.2ms
image 258/1524 /content/data/images/test/test_01391.jpg: 256x320 1 GMC Terrain SUV 2012, 8.3ms
image 259/1524 /content/data/images/test/test_01393.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 7.6ms
image 260/1524 /content/data/images/test/test_01395.jpg: 256x320 1 Honda Accord Coupe 2012, 7.7ms
image 261/1524 /content/data/images/test/test_01401.jpg: 224x320 1 Eagle Talon Hatchback 1998, 10.7ms
image 262/1524 /content/data/images/test/test_01402.jpg: 256x320 1 Volvo 240 Sedan 1993, 9.2ms
image 263/1524 /content/data/images/test/test_01415.jpg: 256x320 1 Hyundai Santa Fe SUV 2012, 8.0ms
image 264/1524 /content/data/images/test/test_01422.jpg: 160x320 1 Volkswagen Golf Hatchback 1991, 7.7ms
image 265/1524 /content/data/images/test/test_01427.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 1 GMC Savana Van 2012, 8.5ms
image 266/1524 /content/data/images/test/test_01432.jpg: 256x320 1 Audi S6 Sedan 2011, 7.5ms
image 267/1524 /content/data/images/test/test_01434.jpg: 256x320 1 GMC Savana Van 2012, 7.6ms
image 268/1524 /content/data/images/test/test_01445.jpg: 192x320 1 Fisker Karma Sedan 2012, 7.6ms
image 269/1524 /content/data/images/test/test_01449.jpg: 256x320 1 Isuzu Ascender SUV 2008, 8.2ms
image 270/1524 /content/data/images/test/test_01456.jpg: 256x320 1 Ford Edge SUV 2012, 10.2ms
image 271/1524 /content/data/images/test/test_01462.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 11.2ms
image 272/1524 /content/data/images/test/test_01463.jpg: 256x320 1 Dodge Charger SRT-8 2009, 8.6ms
image 273/1524 /content/data/images/test/test_01470.jpg: 160x320 1 Ferrari 458 Italia Convertible 2012, 1 Ferrari 458 Italia Coupe 2012, 10.7ms
image 274/1524 /content/data/images/test/test_01481.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 8.2ms
image 275/1524 /content/data/images/test/test_01488.jpg: 224x320 1 FIAT 500 Convertible 2012, 8.2ms
image 276/1524 /content/data/images/test/test_01492.jpg: 224x320 1 Jeep Compass SUV 2012, 7.5ms
image 277/1524 /content/data/images/test/test_01502.jpg: 256x320 1 Ford Mustang Convertible 2007, 8.1ms
image 278/1524 /content/data/images/test/test_01507.jpg: 160x320 1 Hyundai Sonata Hybrid Sedan 2012, 8.0ms
image 279/1524 /content/data/images/test/test_01520.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 7.9ms
image 280/1524 /content/data/images/test/test_01531.jpg: 224x320 1 BMW 3 Series Sedan 2012, 10.2ms
image 281/1524 /content/data/images/test/test_01535.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 7.7ms
image 282/1524 /content/data/images/test/test_01537.jpg: 256x320 1 Hyundai Tucson SUV 2012, 7.3ms
image 283/1524 /content/data/images/test/test_01539.jpg: 224x320 1 Dodge Durango SUV 2012, 7.4ms
image 284/1524 /content/data/images/test/test_01541.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 7.5ms
image 285/1524 /content/data/images/test/test_01548.jpg: 160x320 1 Buick Rainier SUV 2007, 7.3ms
image 286/1524 /content/data/images/test/test_01550.jpg: 224x320 1 Rolls-Royce Phantom Sedan 2012, 9.2ms
image 287/1524 /content/data/images/test/test_01553.jpg: 224x320 1 Chevrolet Silverado 1500 Extended Cab 2012, 7.0ms
image 288/1524 /content/data/images/test/test_01563.jpg: 256x320 1 Acura TSX Sedan 2012, 7.5ms
image 289/1524 /content/data/images/test/test_01570.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 7.5ms
image 290/1524 /content/data/images/test/test_01576.jpg: 256x320 1 Chrysler 300 SRT-8 2010, 7.5ms
image 291/1524 /content/data/images/test/test_01598.jpg: 256x320 1 Dodge Magnum Wagon 2008, 8.2ms
image 292/1524 /content/data/images/test/test_01601.jpg: 256x320 1 Lamborghini Reventon Coupe 2008, 7.7ms
image 293/1524 /content/data/images/test/test_01616.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.4ms
image 294/1524 /content/data/images/test/test_01620.jpg: 192x320 1 Chevrolet Corvette Ron Fellows Edition Z06 2007, 7.8ms
image 295/1524 /content/data/images/test/test_01626.jpg: 224x320 1 Ford Mustang Convertible 2007, 7.7ms
image 296/1524 /content/data/images/test/test_01632.jpg: 224x320 1 Hyundai Veloster Hatchback 2012, 9.3ms
image 297/1524 /content/data/images/test/test_01641.jpg: 288x320 1 Infiniti QX56 SUV 2011, 8.1ms
image 298/1524 /content/data/images/test/test_01647.jpg: 256x320 1 Chevrolet Avalanche Crew Cab 2012, 8.5ms
image 299/1524 /content/data/images/test/test_01650.jpg: 256x320 1 Aston Martin V8 Vantage Convertible 2012, 7.3ms
image 300/1524 /content/data/images/test/test_01653.jpg: 224x320 1 Dodge Challenger SRT8 2011, 7.6ms
image 301/1524 /content/data/images/test/test_01655.jpg: 192x320 1 Ferrari California Convertible 2012, 7.5ms
image 302/1524 /content/data/images/test/test_01659.jpg: 256x320 1 Chrysler Sebring Convertible 2010, 7.9ms
image 303/1524 /content/data/images/test/test_01660.jpg: 256x320 1 Nissan Juke Hatchback 2012, 7.4ms
image 304/1524 /content/data/images/test/test_01661.jpg: 256x320 2 Chevrolet Malibu Sedan 2007s, 7.9ms
image 305/1524 /content/data/images/test/test_01668.jpg: 256x320 1 Mercedes-Benz C-Class Sedan 2012, 7.9ms
image 306/1524 /content/data/images/test/test_01669.jpg: 256x320 1 BMW 3 Series Wagon 2012, 7.8ms
image 307/1524 /content/data/images/test/test_01671.jpg: 256x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 7.5ms
image 308/1524 /content/data/images/test/test_01678.jpg: 256x320 1 GMC Savana Van 2012, 7.6ms
image 309/1524 /content/data/images/test/test_01692.jpg: 224x320 1 Bugatti Veyron 16.4 Convertible 2009, 1 Bugatti Veyron 16.4 Coupe 2009, 8.2ms
image 310/1524 /content/data/images/test/test_01694.jpg: 256x320 1 Audi R8 Coupe 2012, 11.7ms
image 311/1524 /content/data/images/test/test_01695.jpg: 128x320 1 GMC Yukon Hybrid SUV 2012, 8.5ms
image 312/1524 /content/data/images/test/test_01699.jpg: 256x320 1 Hyundai Tucson SUV 2012, 7.9ms
image 313/1524 /content/data/images/test/test_01702.jpg: 224x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 8.4ms
image 314/1524 /content/data/images/test/test_01704.jpg: 256x320 1 Ford GT Coupe 2006, 7.7ms
image 315/1524 /content/data/images/test/test_01709.jpg: 224x320 1 Acura TL Sedan 2012, 7.5ms
image 316/1524 /content/data/images/test/test_01713.jpg: 256x320 1 Chevrolet Corvette Convertible 2012, 7.5ms
image 317/1524 /content/data/images/test/test_01716.jpg: 256x320 1 Aston Martin V8 Vantage Convertible 2012, 1 Chevrolet Corvette ZR1 2012, 7.3ms
image 318/1524 /content/data/images/test/test_01718.jpg: 160x320 2 Suzuki SX4 Hatchback 2012s, 1 Suzuki SX4 Sedan 2012, 7.3ms
image 319/1524 /content/data/images/test/test_01720.jpg: 224x320 1 Chrysler Aspen SUV 2009, 11.3ms
image 320/1524 /content/data/images/test/test_01722.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 8.0ms
image 321/1524 /content/data/images/test/test_01724.jpg: 224x320 1 Hyundai Tucson SUV 2012, 9.2ms
image 322/1524 /content/data/images/test/test_01725.jpg: 256x320 1 Nissan 240SX Coupe 1998, 7.7ms
image 323/1524 /content/data/images/test/test_01726.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 7.6ms
image 324/1524 /content/data/images/test/test_01731.jpg: 256x320 1 Acura ZDX Hatchback 2012, 8.0ms
image 325/1524 /content/data/images/test/test_01747.jpg: 256x320 1 Bentley Continental GT Coupe 2012, 7.8ms
image 326/1524 /content/data/images/test/test_01749.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 7.9ms
image 327/1524 /content/data/images/test/test_01755.jpg: 256x320 1 Dodge Magnum Wagon 2008, 8.0ms
image 328/1524 /content/data/images/test/test_01766.jpg: 224x320 1 Mercedes-Benz E-Class Sedan 2012, 7.4ms
image 329/1524 /content/data/images/test/test_01767.jpg: 256x320 1 Ford Ranger SuperCab 2011, 8.6ms
image 330/1524 /content/data/images/test/test_01769.jpg: 224x320 1 Chevrolet TrailBlazer SS 2009, 7.6ms
image 331/1524 /content/data/images/test/test_01775.jpg: 192x320 1 Buick Regal GS 2012, 7.2ms
image 332/1524 /content/data/images/test/test_01776.jpg: 256x320 1 Chrysler Aspen SUV 2009, 1 Dodge Durango SUV 2007, 7.9ms
image 333/1524 /content/data/images/test/test_01782.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 7.3ms
image 334/1524 /content/data/images/test/test_01783.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 7.5ms
image 335/1524 /content/data/images/test/test_01790.jpg: 256x320 1 Lamborghini Diablo Coupe 2001, 7.8ms
image 336/1524 /content/data/images/test/test_01796.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 9.0ms
image 337/1524 /content/data/images/test/test_01800.jpg: 256x320 1 Toyota Camry Sedan 2012, 7.6ms
image 338/1524 /content/data/images/test/test_01812.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 7.3ms
image 339/1524 /content/data/images/test/test_01819.jpg: 224x320 1 Chrysler Town and Country Minivan 2012, 7.4ms
image 340/1524 /content/data/images/test/test_01828.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 12.3ms
image 341/1524 /content/data/images/test/test_01830.jpg: 288x320 1 BMW X6 SUV 2012, 11.2ms
image 342/1524 /content/data/images/test/test_01838.jpg: 192x320 1 BMW 3 Series Wagon 2012, 1 BMW X5 SUV 2007, 7.5ms
image 343/1524 /content/data/images/test/test_01844.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 7.6ms
image 344/1524 /content/data/images/test/test_01847.jpg: 224x320 1 Plymouth Neon Coupe 1999, 11.1ms
image 345/1524 /content/data/images/test/test_01853.jpg: 224x320 1 Spyker C8 Convertible 2009, 7.1ms
image 346/1524 /content/data/images/test/test_01855.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 7.7ms
image 347/1524 /content/data/images/test/test_01862.jpg: 160x320 1 Mercedes-Benz Sprinter Van 2012, 7.5ms
image 348/1524 /content/data/images/test/test_01872.jpg: 256x320 1 Honda Accord Coupe 2012, 8.1ms
image 349/1524 /content/data/images/test/test_01875.jpg: 256x320 1 Volkswagen Beetle Hatchback 2012, 7.3ms
image 350/1524 /content/data/images/test/test_01883.jpg: 224x320 1 Chrysler Sebring Convertible 2010, 7.6ms
image 351/1524 /content/data/images/test/test_01891.jpg: 224x320 1 Hyundai Veracruz SUV 2012, 7.7ms
image 352/1524 /content/data/images/test/test_01895.jpg: 256x320 1 Audi S4 Sedan 2007, 8.3ms
image 353/1524 /content/data/images/test/test_01900.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 7.3ms
image 354/1524 /content/data/images/test/test_01905.jpg: 224x320 1 Land Rover Range Rover SUV 2012, 7.8ms
image 355/1524 /content/data/images/test/test_01940.jpg: 288x320 1 Chrysler Sebring Convertible 2010, 8.0ms
image 356/1524 /content/data/images/test/test_01945.jpg: 224x320 1 Mercedes-Benz Sprinter Van 2012, 7.8ms
image 357/1524 /content/data/images/test/test_01946.jpg: 160x320 1 AM General Hummer SUV 2000, 7.4ms
image 358/1524 /content/data/images/test/test_01951.jpg: 256x320 1 Ford GT Coupe 2006, 1 Spyker C8 Convertible 2009, 7.7ms
image 359/1524 /content/data/images/test/test_01957.jpg: 288x320 1 Chevrolet Tahoe Hybrid SUV 2012, 7.7ms
image 360/1524 /content/data/images/test/test_01961.jpg: 256x320 1 Ford F-150 Regular Cab 2012, 11.8ms
image 361/1524 /content/data/images/test/test_01965.jpg: 224x320 1 Audi S5 Convertible 2012, 7.7ms
image 362/1524 /content/data/images/test/test_01970.jpg: 224x320 1 HUMMER H3T Crew Cab 2010, 1 HUMMER H2 SUT Crew Cab 2009, 7.9ms
image 363/1524 /content/data/images/test/test_01974.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 7.3ms
image 364/1524 /content/data/images/test/test_01986.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.7ms
image 365/1524 /content/data/images/test/test_01988.jpg: 256x320 1 Rolls-Royce Phantom Sedan 2012, 7.4ms
image 366/1524 /content/data/images/test/test_01996.jpg: 256x320 1 Volkswagen Beetle Hatchback 2012, 7.4ms
image 367/1524 /content/data/images/test/test_02004.jpg: 256x320 1 Honda Accord Sedan 2012, 7.5ms
image 368/1524 /content/data/images/test/test_02009.jpg: 224x320 1 Chevrolet Traverse SUV 2012, 7.4ms
image 369/1524 /content/data/images/test/test_02011.jpg: 224x320 1 Acura Integra Type R 2001, 7.3ms
image 370/1524 /content/data/images/test/test_02016.jpg: 256x320 1 Suzuki SX4 Hatchback 2012, 8.3ms
image 371/1524 /content/data/images/test/test_02024.jpg: 224x320 1 Audi TT Hatchback 2011, 8.2ms
image 372/1524 /content/data/images/test/test_02025.jpg: 224x320 1 Bugatti Veyron 16.4 Convertible 2009, 7.3ms
image 373/1524 /content/data/images/test/test_02026.jpg: 256x320 1 Chevrolet Corvette Ron Fellows Edition Z06 2007, 7.7ms
image 374/1524 /content/data/images/test/test_02027.jpg: 224x320 1 Ford Ranger SuperCab 2011, 7.9ms
image 375/1524 /content/data/images/test/test_02031.jpg: 256x320 1 BMW 6 Series Convertible 2007, 1 BMW M6 Convertible 2010, 7.8ms
image 376/1524 /content/data/images/test/test_02035.jpg: 224x320 1 Acura RL Sedan 2012, 9.5ms
image 377/1524 /content/data/images/test/test_02038.jpg: 224x320 1 Mercedes-Benz S-Class Sedan 2012, 7.3ms
image 378/1524 /content/data/images/test/test_02046.jpg: 160x320 1 Audi 100 Wagon 1994, 7.5ms
image 379/1524 /content/data/images/test/test_02047.jpg: 256x320 1 Chrysler Aspen SUV 2009, 8.2ms
image 380/1524 /content/data/images/test/test_02054.jpg: 256x320 1 Ford Ranger SuperCab 2011, 7.2ms
image 381/1524 /content/data/images/test/test_02055.jpg: 224x320 1 Audi TT RS Coupe 2012, 7.6ms
image 382/1524 /content/data/images/test/test_02058.jpg: 224x320 1 Hyundai Sonata Hybrid Sedan 2012, 9.6ms
image 383/1524 /content/data/images/test/test_02060.jpg: 256x320 1 GMC Savana Van 2012, 7.7ms
image 384/1524 /content/data/images/test/test_02065.jpg: 192x320 1 Suzuki Kizashi Sedan 2012, 7.4ms
image 385/1524 /content/data/images/test/test_02076.jpg: 224x320 1 GMC Acadia SUV 2012, 7.4ms
image 386/1524 /content/data/images/test/test_02077.jpg: 224x320 1 Dodge Durango SUV 2012, 7.3ms
image 387/1524 /content/data/images/test/test_02083.jpg: 256x320 1 Honda Accord Sedan 2012, 7.5ms
image 388/1524 /content/data/images/test/test_02084.jpg: 256x320 1 GMC Terrain SUV 2012, 7.3ms
image 389/1524 /content/data/images/test/test_02093.jpg: 256x320 1 BMW X5 SUV 2007, 7.3ms
image 390/1524 /content/data/images/test/test_02095.jpg: 192x320 1 Dodge Charger Sedan 2012, 7.6ms
image 391/1524 /content/data/images/test/test_02100.jpg: 192x320 1 Hyundai Santa Fe SUV 2012, 7.2ms
image 392/1524 /content/data/images/test/test_02101.jpg: 192x320 1 Aston Martin V8 Vantage Coupe 2012, 1 Aston Martin Virage Coupe 2012, 7.3ms
image 393/1524 /content/data/images/test/test_02106.jpg: 224x320 1 Hyundai Veloster Hatchback 2012, 7.5ms
image 394/1524 /content/data/images/test/test_02108.jpg: 192x320 1 Audi RS 4 Convertible 2008, 8.1ms
image 395/1524 /content/data/images/test/test_02112.jpg: 224x320 1 Bentley Continental GT Coupe 2007, 7.9ms
image 396/1524 /content/data/images/test/test_02113.jpg: 256x320 1 Ford E-Series Wagon Van 2012, 10.7ms
image 397/1524 /content/data/images/test/test_02114.jpg: 256x320 (no detections), 8.0ms
image 398/1524 /content/data/images/test/test_02120.jpg: 224x320 1 Dodge Charger SRT-8 2009, 8.2ms
image 399/1524 /content/data/images/test/test_02121.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 7.2ms
image 400/1524 /content/data/images/test/test_02124.jpg: 256x320 1 Mitsubishi Lancer Sedan 2012, 7.4ms
image 401/1524 /content/data/images/test/test_02126.jpg: 256x320 1 Bugatti Veyron 16.4 Coupe 2009, 7.4ms
image 402/1524 /content/data/images/test/test_02127.jpg: 256x320 1 Volvo XC90 SUV 2007, 7.7ms
image 403/1524 /content/data/images/test/test_02128.jpg: 128x320 1 Toyota 4Runner SUV 2012, 7.1ms
image 404/1524 /content/data/images/test/test_02134.jpg: 256x320 1 Dodge Durango SUV 2007, 7.2ms
image 405/1524 /content/data/images/test/test_02147.jpg: 224x320 1 Jeep Patriot SUV 2012, 8.7ms
image 406/1524 /content/data/images/test/test_02156.jpg: 256x320 1 Land Rover Range Rover SUV 2012, 8.0ms
image 407/1524 /content/data/images/test/test_02157.jpg: 256x320 1 Ford F-450 Super Duty Crew Cab 2012, 7.4ms
image 408/1524 /content/data/images/test/test_02164.jpg: 224x320 1 Audi S4 Sedan 2012, 7.6ms
image 409/1524 /content/data/images/test/test_02165.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 7.6ms
image 410/1524 /content/data/images/test/test_02166.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 7.2ms
image 411/1524 /content/data/images/test/test_02167.jpg: 128x320 1 Audi RS 4 Convertible 2008, 8.4ms
image 412/1524 /content/data/images/test/test_02178.jpg: 224x320 1 Chevrolet Cobalt SS 2010, 7.4ms
image 413/1524 /content/data/images/test/test_02193.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 7.7ms
image 414/1524 /content/data/images/test/test_02199.jpg: 224x320 1 Porsche Panamera Sedan 2012, 7.4ms
image 415/1524 /content/data/images/test/test_02201.jpg: 288x320 1 Hyundai Veloster Hatchback 2012, 7.8ms
image 416/1524 /content/data/images/test/test_02208.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 7.6ms
image 417/1524 /content/data/images/test/test_02209.jpg: 256x320 1 Jeep Patriot SUV 2012, 8.1ms
image 418/1524 /content/data/images/test/test_02234.jpg: 256x320 1 Spyker C8 Coupe 2009, 7.2ms
image 419/1524 /content/data/images/test/test_02238.jpg: 256x320 1 Lincoln Town Car Sedan 2011, 11.9ms
image 420/1524 /content/data/images/test/test_02241.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 7.6ms
image 421/1524 /content/data/images/test/test_02243.jpg: 256x320 1 Bentley Continental GT Coupe 2012, 1 Bentley Continental GT Coupe 2007, 7.5ms
image 422/1524 /content/data/images/test/test_02247.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 7.4ms
image 423/1524 /content/data/images/test/test_02249.jpg: 256x320 1 BMW 1 Series Convertible 2012, 7.6ms
image 424/1524 /content/data/images/test/test_02254.jpg: 256x320 1 Honda Odyssey Minivan 2007, 7.7ms
image 425/1524 /content/data/images/test/test_02261.jpg: 192x320 1 BMW 3 Series Sedan 2012, 7.3ms
image 426/1524 /content/data/images/test/test_02269.jpg: 320x256 1 Lamborghini Reventon Coupe 2008, 11.0ms
image 427/1524 /content/data/images/test/test_02270.jpg: 128x320 1 Bentley Continental Supersports Conv. Convertible 2012, 13.1ms
image 428/1524 /content/data/images/test/test_02279.jpg: 256x320 1 Ford F-450 Super Duty Crew Cab 2012, 8.1ms
image 429/1524 /content/data/images/test/test_02296.jpg: 192x320 1 Hyundai Azera Sedan 2012, 8.8ms
image 430/1524 /content/data/images/test/test_02312.jpg: 224x320 1 Dodge Challenger SRT8 2011, 7.6ms
image 431/1524 /content/data/images/test/test_02327.jpg: 256x320 1 Ford F-150 Regular Cab 2012, 9.1ms
image 432/1524 /content/data/images/test/test_02335.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 11.3ms
image 433/1524 /content/data/images/test/test_02337.jpg: 224x320 1 Land Rover Range Rover SUV 2012, 7.5ms
image 434/1524 /content/data/images/test/test_02343.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.5ms
image 435/1524 /content/data/images/test/test_02351.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 7.3ms
image 436/1524 /content/data/images/test/test_02352.jpg: 256x320 1 Ford Freestar Minivan 2007, 11.1ms
image 437/1524 /content/data/images/test/test_02360.jpg: 288x320 1 Aston Martin V8 Vantage Convertible 2012, 9.0ms
image 438/1524 /content/data/images/test/test_02363.jpg: 224x320 1 Ford Expedition EL SUV 2009, 8.8ms
image 439/1524 /content/data/images/test/test_02372.jpg: 224x320 1 Daewoo Nubira Wagon 2002, 8.3ms
image 440/1524 /content/data/images/test/test_02381.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 8.7ms
image 441/1524 /content/data/images/test/test_02384.jpg: 256x320 1 Dodge Magnum Wagon 2008, 8.9ms
image 442/1524 /content/data/images/test/test_02389.jpg: 224x320 1 BMW Z4 Convertible 2012, 8.5ms
image 443/1524 /content/data/images/test/test_02397.jpg: 256x320 1 Dodge Durango SUV 2012, 8.5ms
image 444/1524 /content/data/images/test/test_02401.jpg: 256x320 1 Hyundai Santa Fe SUV 2012, 8.1ms
image 445/1524 /content/data/images/test/test_02404.jpg: 256x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 8.1ms
image 446/1524 /content/data/images/test/test_02407.jpg: 224x320 1 Bentley Arnage Sedan 2009, 8.5ms
image 447/1524 /content/data/images/test/test_02416.jpg: 192x320 1 Land Rover Range Rover SUV 2012, 8.6ms
image 448/1524 /content/data/images/test/test_02422.jpg: 256x320 1 Hyundai Veracruz SUV 2012, 8.3ms
image 449/1524 /content/data/images/test/test_02424.jpg: 224x320 1 BMW M5 Sedan 2010, 7.9ms
image 450/1524 /content/data/images/test/test_02427.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 8.1ms
image 451/1524 /content/data/images/test/test_02428.jpg: 256x320 1 Buick Rainier SUV 2007, 8.7ms
image 452/1524 /content/data/images/test/test_02434.jpg: 160x320 1 Infiniti QX56 SUV 2011, 8.8ms
image 453/1524 /content/data/images/test/test_02435.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 8.7ms
image 454/1524 /content/data/images/test/test_02436.jpg: 224x320 1 Acura ZDX Hatchback 2012, 8.6ms
image 455/1524 /content/data/images/test/test_02440.jpg: 224x320 1 Audi S4 Sedan 2007, 8.0ms
image 456/1524 /content/data/images/test/test_02443.jpg: 288x320 1 Chevrolet Monte Carlo Coupe 2007, 8.6ms
image 457/1524 /content/data/images/test/test_02472.jpg: 256x320 1 GMC Terrain SUV 2012, 8.4ms
image 458/1524 /content/data/images/test/test_02473.jpg: 256x320 1 Chevrolet Malibu Hybrid Sedan 2010, 7.9ms
image 459/1524 /content/data/images/test/test_02475.jpg: 224x320 1 Chevrolet TrailBlazer SS 2009, 7.7ms
image 460/1524 /content/data/images/test/test_02482.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 8.2ms
image 461/1524 /content/data/images/test/test_02483.jpg: 256x320 1 Dodge Caliber Wagon 2007, 7.7ms
image 462/1524 /content/data/images/test/test_02488.jpg: 192x320 1 Tesla Model S Sedan 2012, 7.2ms
image 463/1524 /content/data/images/test/test_02494.jpg: 224x320 1 Bentley Arnage Sedan 2009, 13.8ms
image 464/1524 /content/data/images/test/test_02499.jpg: 224x320 1 Infiniti QX56 SUV 2011, 8.2ms
image 465/1524 /content/data/images/test/test_02500.jpg: 256x320 1 Honda Accord Coupe 2012, 9.9ms
image 466/1524 /content/data/images/test/test_02504.jpg: 256x320 1 Ferrari 458 Italia Coupe 2012, 8.8ms
image 467/1524 /content/data/images/test/test_02508.jpg: 256x320 1 Chevrolet Malibu Sedan 2007, 7.6ms
image 468/1524 /content/data/images/test/test_02513.jpg: 256x320 1 Tesla Model S Sedan 2012, 7.7ms
image 469/1524 /content/data/images/test/test_02516.jpg: 224x320 1 Mercedes-Benz E-Class Sedan 2012, 7.5ms
image 470/1524 /content/data/images/test/test_02521.jpg: 256x320 1 Ferrari California Convertible 2012, 7.5ms
image 471/1524 /content/data/images/test/test_02523.jpg: 256x320 1 Ford GT Coupe 2006, 7.3ms
image 472/1524 /content/data/images/test/test_02529.jpg: 256x320 1 Isuzu Ascender SUV 2008, 7.1ms
image 473/1524 /content/data/images/test/test_02532.jpg: 192x320 1 FIAT 500 Abarth 2012, 7.1ms
image 474/1524 /content/data/images/test/test_02535.jpg: 256x320 1 Chrysler 300 SRT-8 2010, 7.3ms
image 475/1524 /content/data/images/test/test_02536.jpg: 192x320 1 Bentley Continental Supersports Conv. Convertible 2012, 7.1ms
image 476/1524 /content/data/images/test/test_02544.jpg: 256x320 1 Mercedes-Benz S-Class Sedan 2012, 7.4ms
image 477/1524 /content/data/images/test/test_02551.jpg: 224x320 1 Chevrolet Corvette Convertible 2012, 7.3ms
image 478/1524 /content/data/images/test/test_02553.jpg: 224x320 1 Ferrari California Convertible 2012, 7.3ms
image 479/1524 /content/data/images/test/test_02558.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 7.6ms
image 480/1524 /content/data/images/test/test_02561.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 Chevrolet Silverado 1500 Extended Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.2ms
image 481/1524 /content/data/images/test/test_02564.jpg: 192x320 1 Nissan NV Passenger Van 2012, 7.2ms
image 482/1524 /content/data/images/test/test_02567.jpg: 160x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 8.3ms
image 483/1524 /content/data/images/test/test_02575.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 1 Bentley Continental Flying Spur Sedan 2007, 7.7ms
image 484/1524 /content/data/images/test/test_02582.jpg: 256x320 1 FIAT 500 Convertible 2012, 8.5ms
image 485/1524 /content/data/images/test/test_02583.jpg: 256x320 1 Ford Mustang Convertible 2007, 7.6ms
image 486/1524 /content/data/images/test/test_02586.jpg: 256x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 7.3ms
image 487/1524 /content/data/images/test/test_02590.jpg: 224x320 1 Hyundai Genesis Sedan 2012, 8.9ms
image 488/1524 /content/data/images/test/test_02602.jpg: 224x320 1 AM General Hummer SUV 2000, 7.4ms
image 489/1524 /content/data/images/test/test_02603.jpg: 224x320 1 Jeep Patriot SUV 2012, 1 Jeep Liberty SUV 2012, 7.4ms
image 490/1524 /content/data/images/test/test_02605.jpg: 224x320 1 Isuzu Ascender SUV 2008, 9.3ms
image 491/1524 /content/data/images/test/test_02620.jpg: 224x320 1 Acura TSX Sedan 2012, 7.8ms
image 492/1524 /content/data/images/test/test_02622.jpg: 160x320 1 Volvo C30 Hatchback 2012, 7.5ms
image 493/1524 /content/data/images/test/test_02629.jpg: 224x320 1 Ford F-450 Super Duty Crew Cab 2012, 7.4ms
image 494/1524 /content/data/images/test/test_02632.jpg: 256x320 1 GMC Terrain SUV 2012, 7.5ms
image 495/1524 /content/data/images/test/test_02634.jpg: 256x320 1 HUMMER H2 SUT Crew Cab 2009, 7.2ms
image 496/1524 /content/data/images/test/test_02637.jpg: 224x320 1 Ford E-Series Wagon Van 2012, 7.3ms
image 497/1524 /content/data/images/test/test_02643.jpg: 224x320 1 Bugatti Veyron 16.4 Coupe 2009, 7.6ms
image 498/1524 /content/data/images/test/test_02647.jpg: 256x320 1 Chevrolet Tahoe Hybrid SUV 2012, 7.6ms
image 499/1524 /content/data/images/test/test_02650.jpg: 224x320 1 Bentley Arnage Sedan 2009, 7.4ms
image 500/1524 /content/data/images/test/test_02651.jpg: 224x320 1 Hyundai Azera Sedan 2012, 7.2ms
image 501/1524 /content/data/images/test/test_02656.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 7.4ms
image 502/1524 /content/data/images/test/test_02658.jpg: 224x320 1 Spyker C8 Coupe 2009, 7.4ms
image 503/1524 /content/data/images/test/test_02660.jpg: 224x320 1 Audi R8 Coupe 2012, 11.2ms
image 504/1524 /content/data/images/test/test_02664.jpg: 192x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 7.1ms
image 505/1524 /content/data/images/test/test_02671.jpg: 256x320 1 Geo Metro Convertible 1993, 8.3ms
image 506/1524 /content/data/images/test/test_02672.jpg: 224x320 1 BMW X6 SUV 2012, 7.4ms
image 507/1524 /content/data/images/test/test_02673.jpg: 192x320 1 Ford E-Series Wagon Van 2012, 7.8ms
image 508/1524 /content/data/images/test/test_02679.jpg: 192x320 1 Hyundai Elantra Touring Hatchback 2012, 7.1ms
image 509/1524 /content/data/images/test/test_02680.jpg: 256x320 1 Chrysler Crossfire Convertible 2008, 8.6ms
image 510/1524 /content/data/images/test/test_02682.jpg: 224x320 1 Spyker C8 Coupe 2009, 8.6ms
image 511/1524 /content/data/images/test/test_02683.jpg: 256x320 1 Dodge Charger Sedan 2012, 7.8ms
image 512/1524 /content/data/images/test/test_02685.jpg: 224x320 1 Jeep Compass SUV 2012, 7.8ms
image 513/1524 /content/data/images/test/test_02690.jpg: 256x320 1 Chevrolet Cobalt SS 2010, 7.8ms
image 514/1524 /content/data/images/test/test_02691.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.6ms
image 515/1524 /content/data/images/test/test_02692.jpg: 256x320 1 Dodge Challenger SRT8 2011, 7.4ms
image 516/1524 /content/data/images/test/test_02698.jpg: 224x320 1 Honda Accord Sedan 2012, 7.8ms
image 517/1524 /content/data/images/test/test_02699.jpg: 256x320 1 Scion xD Hatchback 2012, 7.5ms
image 518/1524 /content/data/images/test/test_02714.jpg: 224x320 1 Spyker C8 Coupe 2009, 8.7ms
image 519/1524 /content/data/images/test/test_02717.jpg: 224x320 1 Chevrolet Malibu Hybrid Sedan 2010, 7.4ms
image 520/1524 /content/data/images/test/test_02724.jpg: 224x320 1 Fisker Karma Sedan 2012, 7.5ms
image 521/1524 /content/data/images/test/test_02725.jpg: 224x320 1 Nissan Juke Hatchback 2012, 7.2ms
image 522/1524 /content/data/images/test/test_02726.jpg: 256x320 1 MINI Cooper Roadster Convertible 2012, 10.3ms
image 523/1524 /content/data/images/test/test_02737.jpg: 128x320 1 BMW M3 Coupe 2012, 13.0ms
image 524/1524 /content/data/images/test/test_02751.jpg: 224x320 1 Ferrari 458 Italia Coupe 2012, 7.5ms
image 525/1524 /content/data/images/test/test_02754.jpg: 256x320 1 Chrysler Sebring Convertible 2010, 7.5ms
image 526/1524 /content/data/images/test/test_02758.jpg: 224x320 2 Nissan NV Passenger Van 2012s, 7.8ms
image 527/1524 /content/data/images/test/test_02764.jpg: 224x320 1 Audi A5 Coupe 2012, 7.2ms
image 528/1524 /content/data/images/test/test_02765.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 7.6ms
image 529/1524 /content/data/images/test/test_02777.jpg: 224x320 1 BMW 3 Series Wagon 2012, 10.1ms
image 530/1524 /content/data/images/test/test_02779.jpg: 256x320 1 GMC Terrain SUV 2012, 8.3ms
image 531/1524 /content/data/images/test/test_02783.jpg: 256x320 1 Hyundai Sonata Hybrid Sedan 2012, 1 Hyundai Sonata Sedan 2012, 9.0ms
image 532/1524 /content/data/images/test/test_02794.jpg: 224x320 1 Audi RS 4 Convertible 2008, 7.7ms
image 533/1524 /content/data/images/test/test_02797.jpg: 160x320 1 Chevrolet Camaro Convertible 2012, 8.1ms
image 534/1524 /content/data/images/test/test_02808.jpg: 256x320 1 BMW Z4 Convertible 2012, 7.8ms
image 535/1524 /content/data/images/test/test_02810.jpg: 224x320 1 Ford F-450 Super Duty Crew Cab 2012, 7.6ms
image 536/1524 /content/data/images/test/test_02815.jpg: 224x320 1 Aston Martin V8 Vantage Coupe 2012, 7.8ms
image 537/1524 /content/data/images/test/test_02825.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 1 Mercedes-Benz Sprinter Van 2012, 7.8ms
image 538/1524 /content/data/images/test/test_02845.jpg: 256x320 1 MINI Cooper Roadster Convertible 2012, 7.3ms
image 539/1524 /content/data/images/test/test_02849.jpg: 256x320 1 Bentley Continental GT Coupe 2012, 7.6ms
image 540/1524 /content/data/images/test/test_02853.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 7.5ms
image 541/1524 /content/data/images/test/test_02857.jpg: 256x320 1 Buick Enclave SUV 2012, 8.4ms
image 542/1524 /content/data/images/test/test_02862.jpg: 224x320 1 Maybach Landaulet Convertible 2012, 7.8ms
image 543/1524 /content/data/images/test/test_02874.jpg: 192x320 1 Buick Verano Sedan 2012, 7.5ms
image 544/1524 /content/data/images/test/test_02878.jpg: 224x320 1 Cadillac Escalade EXT Crew Cab 2007, 7.7ms
image 545/1524 /content/data/images/test/test_02879.jpg: 224x320 1 Bentley Mulsanne Sedan 2011, 8.0ms
image 546/1524 /content/data/images/test/test_02881.jpg: 256x320 1 Toyota 4Runner SUV 2012, 7.8ms
image 547/1524 /content/data/images/test/test_02885.jpg: 192x320 1 Chrysler Sebring Convertible 2010, 7.5ms
image 548/1524 /content/data/images/test/test_02886.jpg: 256x320 1 Acura TSX Sedan 2012, 1 Toyota Camry Sedan 2012, 7.2ms
image 549/1524 /content/data/images/test/test_02889.jpg: 256x320 1 Volvo XC90 SUV 2007, 12.0ms
image 550/1524 /content/data/images/test/test_02900.jpg: 160x320 (no detections), 10.5ms
image 551/1524 /content/data/images/test/test_02901.jpg: 256x320 1 Audi S5 Convertible 2012, 7.9ms
image 552/1524 /content/data/images/test/test_02909.jpg: 256x320 1 Chevrolet Malibu Sedan 2007, 1 Chrysler Town and Country Minivan 2012, 7.5ms
image 553/1524 /content/data/images/test/test_02912.jpg: 256x320 1 Scion xD Hatchback 2012, 7.4ms
image 554/1524 /content/data/images/test/test_02918.jpg: 256x320 1 Dodge Charger SRT-8 2009, 7.3ms
image 555/1524 /content/data/images/test/test_02924.jpg: 224x320 1 Ferrari 458 Italia Coupe 2012, 10.0ms
image 556/1524 /content/data/images/test/test_02954.jpg: 256x320 1 Chevrolet Sonic Sedan 2012, 7.8ms
image 557/1524 /content/data/images/test/test_02964.jpg: 256x320 1 Honda Odyssey Minivan 2007, 8.0ms
image 558/1524 /content/data/images/test/test_02971.jpg: 256x320 1 Audi TT Hatchback 2011, 7.6ms
image 559/1524 /content/data/images/test/test_02975.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 7.7ms
image 560/1524 /content/data/images/test/test_02976.jpg: 256x320 1 Audi V8 Sedan 1994, 7.2ms
image 561/1524 /content/data/images/test/test_02979.jpg: 224x320 1 Acura TL Type-S 2008, 12.7ms
image 562/1524 /content/data/images/test/test_02980.jpg: 192x320 1 Infiniti QX56 SUV 2011, 13.4ms
image 563/1524 /content/data/images/test/test_02987.jpg: 256x320 1 Acura TL Type-S 2008, 12.1ms
image 564/1524 /content/data/images/test/test_02989.jpg: 224x320 1 Lamborghini Diablo Coupe 2001, 10.0ms
image 565/1524 /content/data/images/test/test_03000.jpg: 224x320 1 BMW 1 Series Convertible 2012, 9.8ms
image 566/1524 /content/data/images/test/test_03005.jpg: 224x320 1 HUMMER H3T Crew Cab 2010, 9.5ms
image 567/1524 /content/data/images/test/test_03006.jpg: 224x320 1 Jaguar XK XKR 2012, 9.4ms
image 568/1524 /content/data/images/test/test_03011.jpg: 256x320 1 BMW 3 Series Wagon 2012, 10.3ms
image 569/1524 /content/data/images/test/test_03012.jpg: 224x320 1 MINI Cooper Roadster Convertible 2012, 10.5ms
image 570/1524 /content/data/images/test/test_03014.jpg: 192x320 1 Audi S4 Sedan 2007, 15.4ms
image 571/1524 /content/data/images/test/test_03018.jpg: 256x320 1 Nissan Juke Hatchback 2012, 13.6ms
image 572/1524 /content/data/images/test/test_03019.jpg: 288x320 1 Hyundai Santa Fe SUV 2012, 10.6ms
image 573/1524 /content/data/images/test/test_03020.jpg: 256x320 1 Acura TSX Sedan 2012, 10.0ms
image 574/1524 /content/data/images/test/test_03022.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 10.2ms
image 575/1524 /content/data/images/test/test_03026.jpg: 256x320 1 Dodge Caliber Wagon 2007, 10.2ms
image 576/1524 /content/data/images/test/test_03041.jpg: 256x320 1 Ford GT Coupe 2006, 12.2ms
image 577/1524 /content/data/images/test/test_03050.jpg: 256x320 1 Ford F-150 Regular Cab 2012, 10.6ms
image 578/1524 /content/data/images/test/test_03060.jpg: 224x320 1 Ferrari 458 Italia Convertible 2012, 10.0ms
image 579/1524 /content/data/images/test/test_03063.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 10.3ms
image 580/1524 /content/data/images/test/test_03066.jpg: 160x320 1 Ford Edge SUV 2012, 10.9ms
image 581/1524 /content/data/images/test/test_03074.jpg: 192x320 1 Dodge Durango SUV 2012, 13.4ms
image 582/1524 /content/data/images/test/test_03085.jpg: 256x320 1 Chrysler 300 SRT-8 2010, 10.2ms
image 583/1524 /content/data/images/test/test_03086.jpg: 224x320 1 Buick Regal GS 2012, 10.1ms
image 584/1524 /content/data/images/test/test_03088.jpg: 192x320 1 FIAT 500 Abarth 2012, 10.2ms
image 585/1524 /content/data/images/test/test_03089.jpg: 256x320 1 GMC Yukon Hybrid SUV 2012, 10.9ms
image 586/1524 /content/data/images/test/test_03091.jpg: 192x320 1 Ford Fiesta Sedan 2012, 12.7ms
image 587/1524 /content/data/images/test/test_03100.jpg: 224x320 1 Toyota Camry Sedan 2012, 10.3ms
image 588/1524 /content/data/images/test/test_03105.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 10.3ms
image 589/1524 /content/data/images/test/test_03111.jpg: 256x320 1 Hyundai Genesis Sedan 2012, 9.6ms
image 590/1524 /content/data/images/test/test_03115.jpg: 256x320 1 Audi 100 Wagon 1994, 12.5ms
image 591/1524 /content/data/images/test/test_03118.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 12.1ms
image 592/1524 /content/data/images/test/test_03123.jpg: 256x320 1 Mercedes-Benz SL-Class Coupe 2009, 12.9ms
image 593/1524 /content/data/images/test/test_03149.jpg: 256x320 1 Ford F-450 Super Duty Crew Cab 2012, 11.4ms
image 594/1524 /content/data/images/test/test_03173.jpg: 256x320 1 Ford Focus Sedan 2007, 10.3ms
image 595/1524 /content/data/images/test/test_03174.jpg: 256x320 1 Acura TL Sedan 2012, 9.7ms
image 596/1524 /content/data/images/test/test_03184.jpg: 256x320 1 Jeep Wrangler SUV 2012, 9.6ms
image 597/1524 /content/data/images/test/test_03187.jpg: 256x320 1 Dodge Charger SRT-8 2009, 10.2ms
image 598/1524 /content/data/images/test/test_03190.jpg: 160x320 1 Hyundai Veracruz SUV 2012, 10.0ms
image 599/1524 /content/data/images/test/test_03193.jpg: 320x288 1 AM General Hummer SUV 2000, 1 HUMMER H2 SUT Crew Cab 2009, 19.4ms
image 600/1524 /content/data/images/test/test_03197.jpg: 160x320 1 Volkswagen Beetle Hatchback 2012, 10.0ms
image 601/1524 /content/data/images/test/test_03199.jpg: 256x320 1 Aston Martin V8 Vantage Convertible 2012, 10.2ms
image 602/1524 /content/data/images/test/test_03202.jpg: 256x320 1 Ford Ranger SuperCab 2011, 10.5ms
image 603/1524 /content/data/images/test/test_03206.jpg: 256x320 1 Audi S6 Sedan 2011, 9.8ms
image 604/1524 /content/data/images/test/test_03213.jpg: 256x320 1 Dodge Charger SRT-8 2009, 10.0ms
image 605/1524 /content/data/images/test/test_03217.jpg: 256x320 1 Audi S4 Sedan 2007, 9.9ms
image 606/1524 /content/data/images/test/test_03222.jpg: 256x320 1 Ford Focus Sedan 2007, 9.6ms
image 607/1524 /content/data/images/test/test_03229.jpg: 256x320 1 Ford Mustang Convertible 2007, 10.3ms
image 608/1524 /content/data/images/test/test_03232.jpg: 256x320 1 Dodge Journey SUV 2012, 11.2ms
image 609/1524 /content/data/images/test/test_03238.jpg: 256x320 1 Ford Fiesta Sedan 2012, 10.1ms
image 610/1524 /content/data/images/test/test_03241.jpg: 256x320 1 Aston Martin V8 Vantage Coupe 2012, 15.6ms
image 611/1524 /content/data/images/test/test_03244.jpg: 256x320 1 Jaguar XK XKR 2012, 10.1ms
image 612/1524 /content/data/images/test/test_03246.jpg: 160x320 1 AM General Hummer SUV 2000, 9.8ms
image 613/1524 /content/data/images/test/test_03249.jpg: 256x320 1 Dodge Caliber Wagon 2012, 12.0ms
image 614/1524 /content/data/images/test/test_03252.jpg: 224x320 1 Bugatti Veyron 16.4 Coupe 2009, 1 McLaren MP4-12C Coupe 2012, 12.7ms
image 615/1524 /content/data/images/test/test_03253.jpg: 256x320 1 Buick Verano Sedan 2012, 12.6ms
image 616/1524 /content/data/images/test/test_03265.jpg: 224x320 1 BMW 3 Series Sedan 2012, 10.6ms
image 617/1524 /content/data/images/test/test_03269.jpg: 224x320 1 Honda Odyssey Minivan 2012, 9.7ms
image 618/1524 /content/data/images/test/test_03271.jpg: 256x320 1 Nissan NV Passenger Van 2012, 10.0ms
image 619/1524 /content/data/images/test/test_03285.jpg: 224x320 1 FIAT 500 Abarth 2012, 10.4ms
image 620/1524 /content/data/images/test/test_03306.jpg: 224x320 1 Acura RL Sedan 2012, 10.1ms
image 621/1524 /content/data/images/test/test_03311.jpg: 256x320 1 Acura TL Sedan 2012, 10.2ms
image 622/1524 /content/data/images/test/test_03313.jpg: 224x320 1 Dodge Caliber Wagon 2012, 10.4ms
image 623/1524 /content/data/images/test/test_03316.jpg: 224x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 10.9ms
image 624/1524 /content/data/images/test/test_03321.jpg: 224x320 1 Hyundai Elantra Sedan 2007, 1 Hyundai Accent Sedan 2012, 11.8ms
image 625/1524 /content/data/images/test/test_03326.jpg: 256x320 1 Volvo 240 Sedan 1993, 10.1ms
image 626/1524 /content/data/images/test/test_03333.jpg: 224x320 1 Chevrolet Impala Sedan 2007, 10.4ms
image 627/1524 /content/data/images/test/test_03338.jpg: 224x320 1 Ferrari 458 Italia Convertible 2012, 10.0ms
image 628/1524 /content/data/images/test/test_03354.jpg: 224x320 1 BMW 1 Series Coupe 2012, 10.2ms
image 629/1524 /content/data/images/test/test_03355.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 10.1ms
image 630/1524 /content/data/images/test/test_03356.jpg: 256x320 1 Chrysler 300 SRT-8 2010, 10.1ms
image 631/1524 /content/data/images/test/test_03359.jpg: 224x320 1 BMW M5 Sedan 2010, 1 Suzuki Kizashi Sedan 2012, 10.7ms
image 632/1524 /content/data/images/test/test_03366.jpg: 256x320 1 GMC Acadia SUV 2012, 10.4ms
image 633/1524 /content/data/images/test/test_03375.jpg: 256x320 1 Hyundai Tucson SUV 2012, 10.0ms
image 634/1524 /content/data/images/test/test_03398.jpg: 224x320 1 Audi S4 Sedan 2012, 10.3ms
image 635/1524 /content/data/images/test/test_03406.jpg: 224x320 1 Buick Rainier SUV 2007, 10.1ms
image 636/1524 /content/data/images/test/test_03407.jpg: 256x320 1 Buick Regal GS 2012, 10.2ms
image 637/1524 /content/data/images/test/test_03409.jpg: 224x320 1 Bugatti Veyron 16.4 Coupe 2009, 13.1ms
image 638/1524 /content/data/images/test/test_03414.jpg: 224x320 1 Hyundai Accent Sedan 2012, 11.3ms
image 639/1524 /content/data/images/test/test_03421.jpg: 256x320 1 Chrysler Sebring Convertible 2010, 10.8ms
image 640/1524 /content/data/images/test/test_03437.jpg: 256x320 1 Dodge Journey SUV 2012, 10.2ms
image 641/1524 /content/data/images/test/test_03449.jpg: 320x320 1 Ferrari FF Coupe 2012, 14.7ms
image 642/1524 /content/data/images/test/test_03450.jpg: 224x320 1 Suzuki Kizashi Sedan 2012, 1 Volvo C30 Hatchback 2012, 10.1ms
image 643/1524 /content/data/images/test/test_03452.jpg: 256x320 1 Jeep Compass SUV 2012, 12.0ms
image 644/1524 /content/data/images/test/test_03454.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 10.4ms
image 645/1524 /content/data/images/test/test_03456.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 10.7ms
image 646/1524 /content/data/images/test/test_03460.jpg: 288x320 1 Scion xD Hatchback 2012, 10.7ms
image 647/1524 /content/data/images/test/test_03463.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 15.2ms
image 648/1524 /content/data/images/test/test_03464.jpg: 256x320 1 Eagle Talon Hatchback 1998, 13.1ms
image 649/1524 /content/data/images/test/test_03472.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 11.4ms
image 650/1524 /content/data/images/test/test_03473.jpg: 288x320 1 Hyundai Santa Fe SUV 2012, 11.5ms
image 651/1524 /content/data/images/test/test_03483.jpg: 224x320 1 smart fortwo Convertible 2012, 11.3ms
image 652/1524 /content/data/images/test/test_03485.jpg: 256x320 1 Dodge Caliber Wagon 2012, 13.5ms
image 653/1524 /content/data/images/test/test_03488.jpg: 256x320 1 Mazda Tribute SUV 2011, 14.1ms
image 654/1524 /content/data/images/test/test_03496.jpg: 256x320 1 HUMMER H3T Crew Cab 2010, 14.7ms
image 655/1524 /content/data/images/test/test_03497.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 12.6ms
image 656/1524 /content/data/images/test/test_03500.jpg: 224x320 1 Volvo XC90 SUV 2007, 13.7ms
image 657/1524 /content/data/images/test/test_03503.jpg: 192x320 1 Volkswagen Beetle Hatchback 2012, 14.5ms
image 658/1524 /content/data/images/test/test_03505.jpg: 224x320 1 BMW X6 SUV 2012, 15.4ms
image 659/1524 /content/data/images/test/test_03508.jpg: 160x320 1 Volvo C30 Hatchback 2012, 14.8ms
image 660/1524 /content/data/images/test/test_03512.jpg: 256x320 1 Hyundai Santa Fe SUV 2012, 13.9ms
image 661/1524 /content/data/images/test/test_03522.jpg: 256x320 1 Chevrolet Avalanche Crew Cab 2012, 13.0ms
image 662/1524 /content/data/images/test/test_03523.jpg: 256x320 1 Bentley Mulsanne Sedan 2011, 19.2ms
image 663/1524 /content/data/images/test/test_03526.jpg: 224x320 1 Lamborghini Aventador Coupe 2012, 13.8ms
image 664/1524 /content/data/images/test/test_03534.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 13.6ms
image 665/1524 /content/data/images/test/test_03543.jpg: 256x320 1 Aston Martin Virage Coupe 2012, 14.3ms
image 666/1524 /content/data/images/test/test_03546.jpg: 256x320 1 Toyota Sequoia SUV 2012, 20.2ms
image 667/1524 /content/data/images/test/test_03552.jpg: 224x320 1 Audi 100 Wagon 1994, 14.0ms
image 668/1524 /content/data/images/test/test_03561.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 13.6ms
image 669/1524 /content/data/images/test/test_03566.jpg: 256x320 1 Dodge Charger Sedan 2012, 12.9ms
image 670/1524 /content/data/images/test/test_03569.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 14.2ms
image 671/1524 /content/data/images/test/test_03573.jpg: 256x320 1 Land Rover LR2 SUV 2012, 19.1ms
image 672/1524 /content/data/images/test/test_03601.jpg: 224x320 1 Hyundai Veracruz SUV 2012, 1 Suzuki SX4 Hatchback 2012, 24.3ms
image 673/1524 /content/data/images/test/test_03604.jpg: 256x320 1 Dodge Caliber Wagon 2007, 13.8ms
image 674/1524 /content/data/images/test/test_03608.jpg: 256x320 1 Audi TTS Coupe 2012, 1 Audi TT Hatchback 2011, 13.3ms
image 675/1524 /content/data/images/test/test_03622.jpg: 256x320 2 Ford GT Coupe 2006s, 13.2ms
image 676/1524 /content/data/images/test/test_03628.jpg: 256x320 1 Audi A5 Coupe 2012, 13.1ms
image 677/1524 /content/data/images/test/test_03630.jpg: 256x320 1 Honda Odyssey Minivan 2007, 15.0ms
image 678/1524 /content/data/images/test/test_03641.jpg: 256x320 1 Ford Freestar Minivan 2007, 12.1ms
image 679/1524 /content/data/images/test/test_03643.jpg: 256x320 1 Ford Mustang Convertible 2007, 13.9ms
image 680/1524 /content/data/images/test/test_03647.jpg: 256x320 1 Audi A5 Coupe 2012, 1 Audi S5 Coupe 2012, 13.5ms
image 681/1524 /content/data/images/test/test_03648.jpg: 256x320 1 Porsche Panamera Sedan 2012, 13.1ms
image 682/1524 /content/data/images/test/test_03655.jpg: 256x320 1 Buick Rainier SUV 2007, 13.2ms
image 683/1524 /content/data/images/test/test_03658.jpg: 224x320 1 Hyundai Genesis Sedan 2012, 13.2ms
image 684/1524 /content/data/images/test/test_03662.jpg: 256x320 1 Audi 100 Wagon 1994, 1 Lincoln Town Car Sedan 2011, 13.6ms
image 685/1524 /content/data/images/test/test_03666.jpg: 192x320 1 Volkswagen Beetle Hatchback 2012, 12.9ms
image 686/1524 /content/data/images/test/test_03667.jpg: 256x320 1 Audi S4 Sedan 2007, 13.3ms
image 687/1524 /content/data/images/test/test_03669.jpg: 224x320 1 Hyundai Azera Sedan 2012, 13.5ms
image 688/1524 /content/data/images/test/test_03671.jpg: 256x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 14.3ms
image 689/1524 /content/data/images/test/test_03674.jpg: 224x320 1 Tesla Model S Sedan 2012, 13.7ms
image 690/1524 /content/data/images/test/test_03678.jpg: 256x320 2 GMC Savana Van 2012s, 18.9ms
image 691/1524 /content/data/images/test/test_03684.jpg: 256x320 1 Audi A5 Coupe 2012, 13.6ms
image 692/1524 /content/data/images/test/test_03685.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 13.6ms
image 693/1524 /content/data/images/test/test_03695.jpg: 224x320 (no detections), 13.4ms
image 694/1524 /content/data/images/test/test_03697.jpg: 224x320 1 Fisker Karma Sedan 2012, 12.9ms
image 695/1524 /content/data/images/test/test_03698.jpg: 256x320 1 Audi TT Hatchback 2011, 1 Audi TT RS Coupe 2012, 13.2ms
image 696/1524 /content/data/images/test/test_03706.jpg: 256x320 1 Ferrari FF Coupe 2012, 13.0ms
image 697/1524 /content/data/images/test/test_03708.jpg: 256x320 1 Suzuki Aerio Sedan 2007, 17.6ms
image 698/1524 /content/data/images/test/test_03709.jpg: 256x320 1 Plymouth Neon Coupe 1999, 14.8ms
image 699/1524 /content/data/images/test/test_03712.jpg: 224x320 1 Aston Martin V8 Vantage Convertible 2012, 15.5ms
image 700/1524 /content/data/images/test/test_03713.jpg: 224x320 1 Chevrolet Corvette Convertible 2012, 13.5ms
image 701/1524 /content/data/images/test/test_03714.jpg: 192x320 1 Rolls-Royce Phantom Sedan 2012, 13.1ms
image 702/1524 /content/data/images/test/test_03719.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 14.0ms
image 703/1524 /content/data/images/test/test_03720.jpg: 224x320 1 HUMMER H3T Crew Cab 2010, 13.1ms
image 704/1524 /content/data/images/test/test_03722.jpg: 224x320 1 BMW M3 Coupe 2012, 14.1ms
image 705/1524 /content/data/images/test/test_03723.jpg: 256x320 1 Nissan Juke Hatchback 2012, 14.4ms
image 706/1524 /content/data/images/test/test_03726.jpg: 224x320 1 Fisker Karma Sedan 2012, 13.5ms
image 707/1524 /content/data/images/test/test_03727.jpg: 256x320 1 Chevrolet TrailBlazer SS 2009, 12.4ms
image 708/1524 /content/data/images/test/test_03730.jpg: 224x320 1 Chrysler Aspen SUV 2009, 16.1ms
image 709/1524 /content/data/images/test/test_03733.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 23.0ms
image 710/1524 /content/data/images/test/test_03736.jpg: 256x320 1 BMW ActiveHybrid 5 Sedan 2012, 1 BMW 3 Series Wagon 2012, 10.9ms
image 711/1524 /content/data/images/test/test_03737.jpg: 256x320 1 Chevrolet Malibu Sedan 2007, 12.8ms
image 712/1524 /content/data/images/test/test_03742.jpg: 192x320 1 smart fortwo Convertible 2012, 12.6ms
image 713/1524 /content/data/images/test/test_03744.jpg: 256x320 1 Audi R8 Coupe 2012, 14.6ms
image 714/1524 /content/data/images/test/test_03762.jpg: 224x320 1 Hyundai Elantra Touring Hatchback 2012, 1 Suzuki SX4 Hatchback 2012, 12.0ms
image 715/1524 /content/data/images/test/test_03765.jpg: 256x320 1 Isuzu Ascender SUV 2008, 11.1ms
image 716/1524 /content/data/images/test/test_03770.jpg: 224x320 1 Volvo C30 Hatchback 2012, 11.9ms
image 717/1524 /content/data/images/test/test_03771.jpg: 256x320 1 Dodge Durango SUV 2007, 11.0ms
image 718/1524 /content/data/images/test/test_03775.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 10.6ms
image 719/1524 /content/data/images/test/test_03780.jpg: 256x320 1 BMW 6 Series Convertible 2007, 1 BMW M6 Convertible 2010, 10.4ms
image 720/1524 /content/data/images/test/test_03790.jpg: 224x320 1 Cadillac SRX SUV 2012, 14.8ms
image 721/1524 /content/data/images/test/test_03794.jpg: 256x320 1 Chevrolet Malibu Sedan 2007, 12.7ms
image 722/1524 /content/data/images/test/test_03799.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 10.2ms
image 723/1524 /content/data/images/test/test_03806.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 10.6ms
image 724/1524 /content/data/images/test/test_03808.jpg: 192x320 1 Audi S5 Coupe 2012, 10.7ms
image 725/1524 /content/data/images/test/test_03809.jpg: 256x320 1 Toyota Corolla Sedan 2012, 10.9ms
image 726/1524 /content/data/images/test/test_03811.jpg: 192x320 1 Infiniti G Coupe IPL 2012, 10.3ms
image 727/1524 /content/data/images/test/test_03823.jpg: 256x320 1 Nissan 240SX Coupe 1998, 10.6ms
image 728/1524 /content/data/images/test/test_03826.jpg: 256x320 1 Ford Freestar Minivan 2007, 10.4ms
image 729/1524 /content/data/images/test/test_03830.jpg: 192x320 1 Nissan NV Passenger Van 2012, 13.1ms
image 730/1524 /content/data/images/test/test_03841.jpg: 256x320 1 Honda Accord Sedan 2012, 12.8ms
image 731/1524 /content/data/images/test/test_03847.jpg: 256x320 1 Toyota Sequoia SUV 2012, 12.5ms
image 732/1524 /content/data/images/test/test_03848.jpg: 224x320 1 Chevrolet Sonic Sedan 2012, 10.7ms
image 733/1524 /content/data/images/test/test_03852.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 10.3ms
image 734/1524 /content/data/images/test/test_03855.jpg: 256x320 1 Ford Fiesta Sedan 2012, 11.2ms
image 735/1524 /content/data/images/test/test_03861.jpg: 256x320 1 BMW M6 Convertible 2010, 9.9ms
image 736/1524 /content/data/images/test/test_03883.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 Chevrolet Silverado 1500 Extended Cab 2012, 12.0ms
image 737/1524 /content/data/images/test/test_03913.jpg: 256x320 1 Nissan 240SX Coupe 1998, 10.7ms
image 738/1524 /content/data/images/test/test_03915.jpg: 192x320 1 Mercedes-Benz S-Class Sedan 2012, 10.7ms
image 739/1524 /content/data/images/test/test_03920.jpg: 224x320 1 Acura Integra Type R 2001, 10.6ms
image 740/1524 /content/data/images/test/test_03922.jpg: 256x320 1 Ford E-Series Wagon Van 2012, 10.5ms
image 741/1524 /content/data/images/test/test_03926.jpg: 192x320 1 Hyundai Azera Sedan 2012, 10.6ms
image 742/1524 /content/data/images/test/test_03932.jpg: 224x320 1 Bentley Mulsanne Sedan 2011, 10.3ms
image 743/1524 /content/data/images/test/test_03937.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 11.5ms
image 744/1524 /content/data/images/test/test_03938.jpg: 192x320 1 Aston Martin Virage Coupe 2012, 11.6ms
image 745/1524 /content/data/images/test/test_03946.jpg: 256x320 1 Buick Verano Sedan 2012, 11.2ms
image 746/1524 /content/data/images/test/test_03948.jpg: 224x320 1 Dodge Caliber Wagon 2007, 17.5ms
image 747/1524 /content/data/images/test/test_03949.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 21.8ms
image 748/1524 /content/data/images/test/test_03951.jpg: 224x320 1 Suzuki SX4 Hatchback 2012, 21.7ms
image 749/1524 /content/data/images/test/test_03952.jpg: 256x320 1 Mercedes-Benz C-Class Sedan 2012, 16.8ms
image 750/1524 /content/data/images/test/test_03954.jpg: 256x320 1 Dodge Durango SUV 2012, 19.8ms
image 751/1524 /content/data/images/test/test_03955.jpg: 224x320 1 Chevrolet Corvette Convertible 2012, 14.2ms
image 752/1524 /content/data/images/test/test_03956.jpg: 320x288 1 Chevrolet Corvette ZR1 2012, 17.5ms
image 753/1524 /content/data/images/test/test_03960.jpg: 256x320 1 Eagle Talon Hatchback 1998, 13.6ms
image 754/1524 /content/data/images/test/test_03962.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 Chevrolet Camaro Convertible 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 14.0ms
image 755/1524 /content/data/images/test/test_03963.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 1 GMC Savana Van 2012, 10.6ms
image 756/1524 /content/data/images/test/test_03972.jpg: 224x320 1 GMC Terrain SUV 2012, 12.7ms
image 757/1524 /content/data/images/test/test_03976.jpg: 256x320 1 Suzuki Aerio Sedan 2007, 13.3ms
image 758/1524 /content/data/images/test/test_03984.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 10.9ms
image 759/1524 /content/data/images/test/test_03989.jpg: 256x320 1 Suzuki Kizashi Sedan 2012, 15.3ms
image 760/1524 /content/data/images/test/test_03991.jpg: 224x320 1 Suzuki Kizashi Sedan 2012, 10.9ms
image 761/1524 /content/data/images/test/test_03992.jpg: 256x320 1 Hyundai Veloster Hatchback 2012, 11.5ms
image 762/1524 /content/data/images/test/test_03994.jpg: 160x320 1 Honda Accord Coupe 2012, 10.9ms
image 763/1524 /content/data/images/test/test_03999.jpg: 224x320 1 Ford GT Coupe 2006, 12.1ms
image 764/1524 /content/data/images/test/test_04008.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 10.9ms
image 765/1524 /content/data/images/test/test_04010.jpg: 128x320 1 Aston Martin V8 Vantage Convertible 2012, 13.4ms
image 766/1524 /content/data/images/test/test_04019.jpg: 192x320 1 Chrysler Town and Country Minivan 2012, 10.0ms
image 767/1524 /content/data/images/test/test_04021.jpg: 256x320 1 Audi A5 Coupe 2012, 18.4ms
image 768/1524 /content/data/images/test/test_04029.jpg: 192x320 1 Audi 100 Sedan 1994, 11.0ms
image 769/1524 /content/data/images/test/test_04036.jpg: 256x320 1 GMC Savana Van 2012, 11.3ms
image 770/1524 /content/data/images/test/test_04048.jpg: 224x320 1 Hyundai Tucson SUV 2012, 11.0ms
image 771/1524 /content/data/images/test/test_04056.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 11.0ms
image 772/1524 /content/data/images/test/test_04063.jpg: 192x320 1 Jeep Patriot SUV 2012, 10.9ms
image 773/1524 /content/data/images/test/test_04068.jpg: 256x320 1 Audi S4 Sedan 2007, 19.8ms
image 774/1524 /content/data/images/test/test_04071.jpg: 256x320 1 Bentley Arnage Sedan 2009, 12.0ms
image 775/1524 /content/data/images/test/test_04072.jpg: 192x320 1 Ferrari 458 Italia Coupe 2012, 13.8ms
image 776/1524 /content/data/images/test/test_04073.jpg: 256x320 1 Nissan Leaf Hatchback 2012, 15.3ms
image 777/1524 /content/data/images/test/test_04081.jpg: 256x320 1 Hyundai Genesis Sedan 2012, 10.5ms
image 778/1524 /content/data/images/test/test_04082.jpg: 224x320 1 Audi S5 Convertible 2012, 11.0ms
image 779/1524 /content/data/images/test/test_04089.jpg: 256x320 1 Chevrolet TrailBlazer SS 2009, 10.9ms
image 780/1524 /content/data/images/test/test_04091.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 10.4ms
image 781/1524 /content/data/images/test/test_04094.jpg: 224x320 1 BMW 6 Series Convertible 2007, 12.8ms
image 782/1524 /content/data/images/test/test_04102.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 11.2ms
image 783/1524 /content/data/images/test/test_04106.jpg: 224x320 1 Tesla Model S Sedan 2012, 11.6ms
image 784/1524 /content/data/images/test/test_04112.jpg: 224x320 1 Volvo C30 Hatchback 2012, 10.2ms
image 785/1524 /content/data/images/test/test_04115.jpg: 256x320 1 Honda Odyssey Minivan 2007, 22.9ms
image 786/1524 /content/data/images/test/test_04125.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 11.6ms
image 787/1524 /content/data/images/test/test_04138.jpg: 256x320 1 Bugatti Veyron 16.4 Convertible 2009, 10.3ms
image 788/1524 /content/data/images/test/test_04146.jpg: 256x320 1 GMC Savana Van 2012, 10.2ms
image 789/1524 /content/data/images/test/test_04148.jpg: 256x320 1 BMW 1 Series Convertible 2012, 10.0ms
image 790/1524 /content/data/images/test/test_04157.jpg: 256x320 1 Ford Freestar Minivan 2007, 11.1ms
image 791/1524 /content/data/images/test/test_04163.jpg: 224x320 1 Bentley Arnage Sedan 2009, 10.8ms
image 792/1524 /content/data/images/test/test_04171.jpg: 256x320 1 Dodge Magnum Wagon 2008, 10.9ms
image 793/1524 /content/data/images/test/test_04176.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 12.4ms
image 794/1524 /content/data/images/test/test_04178.jpg: 256x320 1 Aston Martin Virage Convertible 2012, 18.9ms
image 795/1524 /content/data/images/test/test_04182.jpg: 224x320 1 Dodge Journey SUV 2012, 12.6ms
image 796/1524 /content/data/images/test/test_04184.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 14.6ms
image 797/1524 /content/data/images/test/test_04186.jpg: 224x320 1 Rolls-Royce Phantom Sedan 2012, 11.3ms
image 798/1524 /content/data/images/test/test_04191.jpg: 224x320 1 Dodge Charger Sedan 2012, 10.9ms
image 799/1524 /content/data/images/test/test_04193.jpg: 256x320 1 Dodge Durango SUV 2007, 13.3ms
image 800/1524 /content/data/images/test/test_04194.jpg: 256x320 1 Plymouth Neon Coupe 1999, 15.3ms
image 801/1524 /content/data/images/test/test_04195.jpg: 256x320 1 Ford Expedition EL SUV 2009, 11.2ms
image 802/1524 /content/data/images/test/test_04201.jpg: 224x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 10.5ms
image 803/1524 /content/data/images/test/test_04208.jpg: 224x320 1 Lamborghini Reventon Coupe 2008, 14.2ms
image 804/1524 /content/data/images/test/test_04212.jpg: 256x320 1 Dodge Caravan Minivan 1997, 12.5ms
image 805/1524 /content/data/images/test/test_04217.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 11.9ms
image 806/1524 /content/data/images/test/test_04226.jpg: 256x320 1 Dodge Journey SUV 2012, 13.6ms
image 807/1524 /content/data/images/test/test_04227.jpg: 256x320 1 Chevrolet Corvette Convertible 2012, 14.9ms
image 808/1524 /content/data/images/test/test_04229.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 13.5ms
image 809/1524 /content/data/images/test/test_04231.jpg: 256x320 1 Ford Focus Sedan 2007, 14.1ms
image 810/1524 /content/data/images/test/test_04233.jpg: 224x320 1 Jaguar XK XKR 2012, 13.2ms
image 811/1524 /content/data/images/test/test_04242.jpg: 224x320 1 Daewoo Nubira Wagon 2002, 11.8ms
image 812/1524 /content/data/images/test/test_04251.jpg: 224x320 1 Chrysler PT Cruiser Convertible 2008, 13.8ms
image 813/1524 /content/data/images/test/test_04259.jpg: 224x320 1 BMW M5 Sedan 2010, 13.0ms
image 814/1524 /content/data/images/test/test_04260.jpg: 224x320 1 Chrysler Sebring Convertible 2010, 12.6ms
image 815/1524 /content/data/images/test/test_04261.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 16.9ms
image 816/1524 /content/data/images/test/test_04267.jpg: 192x320 1 Aston Martin V8 Vantage Coupe 2012, 14.0ms
image 817/1524 /content/data/images/test/test_04268.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 13.5ms
image 818/1524 /content/data/images/test/test_04283.jpg: 224x320 1 Cadillac Escalade EXT Crew Cab 2007, 13.5ms
image 819/1524 /content/data/images/test/test_04286.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 17.4ms
image 820/1524 /content/data/images/test/test_04300.jpg: 256x320 1 Buick Enclave SUV 2012, 14.3ms
image 821/1524 /content/data/images/test/test_04326.jpg: 256x320 1 Dodge Caravan Minivan 1997, 13.0ms
image 822/1524 /content/data/images/test/test_04329.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 13.0ms
image 823/1524 /content/data/images/test/test_04332.jpg: 224x320 1 Chrysler Aspen SUV 2009, 12.7ms
image 824/1524 /content/data/images/test/test_04334.jpg: 160x320 1 FIAT 500 Convertible 2012, 19.2ms
image 825/1524 /content/data/images/test/test_04335.jpg: 224x320 1 Bentley Continental Flying Spur Sedan 2007, 12.9ms
image 826/1524 /content/data/images/test/test_04340.jpg: 256x320 1 BMW M6 Convertible 2010, 13.6ms
image 827/1524 /content/data/images/test/test_04344.jpg: 256x320 1 Lamborghini Diablo Coupe 2001, 12.5ms
image 828/1524 /content/data/images/test/test_04345.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 12.8ms
image 829/1524 /content/data/images/test/test_04347.jpg: 256x320 1 Ford Focus Sedan 2007, 15.3ms
image 830/1524 /content/data/images/test/test_04352.jpg: 256x320 1 Mazda Tribute SUV 2011, 13.9ms
image 831/1524 /content/data/images/test/test_04353.jpg: 192x320 1 Rolls-Royce Ghost Sedan 2012, 13.5ms
image 832/1524 /content/data/images/test/test_04355.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 13.9ms
image 833/1524 /content/data/images/test/test_04360.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 20.5ms
image 834/1524 /content/data/images/test/test_04361.jpg: 224x320 1 Acura TL Type-S 2008, 14.0ms
image 835/1524 /content/data/images/test/test_04362.jpg: 224x320 1 Aston Martin V8 Vantage Coupe 2012, 13.2ms
image 836/1524 /content/data/images/test/test_04370.jpg: 224x320 1 Infiniti G Coupe IPL 2012, 14.1ms
image 837/1524 /content/data/images/test/test_04373.jpg: 192x320 1 Ford Edge SUV 2012, 13.1ms
image 838/1524 /content/data/images/test/test_04374.jpg: 224x320 1 Mercedes-Benz 300-Class Convertible 1993, 40.4ms
image 839/1524 /content/data/images/test/test_04377.jpg: 256x320 1 Honda Odyssey Minivan 2012, 14.5ms
image 840/1524 /content/data/images/test/test_04378.jpg: 224x320 1 Audi 100 Wagon 1994, 19.3ms
image 841/1524 /content/data/images/test/test_04379.jpg: 224x320 1 Honda Accord Sedan 2012, 13.6ms
image 842/1524 /content/data/images/test/test_04393.jpg: 160x320 1 Mitsubishi Lancer Sedan 2012, 13.5ms
image 843/1524 /content/data/images/test/test_04397.jpg: 256x320 1 Volvo 240 Sedan 1993, 13.7ms
image 844/1524 /content/data/images/test/test_04410.jpg: 256x320 1 Jeep Compass SUV 2012, 14.0ms
image 845/1524 /content/data/images/test/test_04423.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 13.2ms
image 846/1524 /content/data/images/test/test_04424.jpg: 256x320 1 Aston Martin V8 Vantage Convertible 2012, 13.2ms
image 847/1524 /content/data/images/test/test_04427.jpg: 256x320 1 Acura TL Sedan 2012, 13.2ms
image 848/1524 /content/data/images/test/test_04432.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 14.8ms
image 849/1524 /content/data/images/test/test_04434.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 1 HUMMER H2 SUT Crew Cab 2009, 13.3ms
image 850/1524 /content/data/images/test/test_04440.jpg: 224x320 1 GMC Canyon Extended Cab 2012, 14.1ms
image 851/1524 /content/data/images/test/test_04443.jpg: 192x320 1 FIAT 500 Abarth 2012, 19.0ms
image 852/1524 /content/data/images/test/test_04450.jpg: 256x320 1 BMW M6 Convertible 2010, 13.6ms
image 853/1524 /content/data/images/test/test_04454.jpg: 256x320 1 Rolls-Royce Ghost Sedan 2012, 12.9ms
image 854/1524 /content/data/images/test/test_04455.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 13.2ms
image 855/1524 /content/data/images/test/test_04461.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 12.7ms
image 856/1524 /content/data/images/test/test_04462.jpg: 192x320 1 Chrysler 300 SRT-8 2010, 12.9ms
image 857/1524 /content/data/images/test/test_04469.jpg: 224x320 1 Jeep Compass SUV 2012, 13.6ms
image 858/1524 /content/data/images/test/test_04474.jpg: 192x320 1 Spyker C8 Convertible 2009, 12.9ms
image 859/1524 /content/data/images/test/test_04477.jpg: 224x320 1 Aston Martin V8 Vantage Coupe 2012, 13.0ms
image 860/1524 /content/data/images/test/test_04480.jpg: 128x320 1 Toyota Corolla Sedan 2012, 12.4ms
image 861/1524 /content/data/images/test/test_04491.jpg: 256x320 1 Nissan 240SX Coupe 1998, 12.8ms
image 862/1524 /content/data/images/test/test_04493.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 12.0ms
image 863/1524 /content/data/images/test/test_04507.jpg: 224x320 1 Rolls-Royce Phantom Sedan 2012, 12.3ms
image 864/1524 /content/data/images/test/test_04509.jpg: 224x320 1 Bugatti Veyron 16.4 Coupe 2009, 18.9ms
image 865/1524 /content/data/images/test/test_04515.jpg: 256x320 1 Volvo XC90 SUV 2007, 13.2ms
image 866/1524 /content/data/images/test/test_04517.jpg: 224x320 1 Acura ZDX Hatchback 2012, 15.8ms
image 867/1524 /content/data/images/test/test_04521.jpg: 224x320 1 Jaguar XK XKR 2012, 13.4ms
image 868/1524 /content/data/images/test/test_04532.jpg: 224x320 1 Toyota Corolla Sedan 2012, 14.0ms
image 869/1524 /content/data/images/test/test_04535.jpg: 256x320 1 Plymouth Neon Coupe 1999, 15.2ms
image 870/1524 /content/data/images/test/test_04540.jpg: 256x320 1 BMW X3 SUV 2012, 21.0ms
image 871/1524 /content/data/images/test/test_04542.jpg: 256x320 1 Mercedes-Benz S-Class Sedan 2012, 13.6ms
image 872/1524 /content/data/images/test/test_04546.jpg: 128x320 1 Audi 100 Wagon 1994, 15.0ms
image 873/1524 /content/data/images/test/test_04555.jpg: 224x320 1 BMW 1 Series Coupe 2012, 13.9ms
image 874/1524 /content/data/images/test/test_04559.jpg: 160x320 1 Dodge Durango SUV 2012, 14.5ms
image 875/1524 /content/data/images/test/test_04563.jpg: 192x320 1 Spyker C8 Convertible 2009, 13.4ms
image 876/1524 /content/data/images/test/test_04565.jpg: 256x320 1 Chevrolet HHR SS 2010, 17.8ms
image 877/1524 /content/data/images/test/test_04569.jpg: 256x320 1 Volvo 240 Sedan 1993, 12.7ms
image 878/1524 /content/data/images/test/test_04573.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 13.4ms
image 879/1524 /content/data/images/test/test_04579.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 1 Chevrolet Avalanche Crew Cab 2012, 13.0ms
image 880/1524 /content/data/images/test/test_04583.jpg: 224x320 1 Acura ZDX Hatchback 2012, 13.7ms
image 881/1524 /content/data/images/test/test_04584.jpg: 256x320 1 Ferrari California Convertible 2012, 13.2ms
image 882/1524 /content/data/images/test/test_04585.jpg: 224x320 1 Audi TT Hatchback 2011, 12.5ms
image 883/1524 /content/data/images/test/test_04591.jpg: 256x320 1 Ford E-Series Wagon Van 2012, 13.0ms
image 884/1524 /content/data/images/test/test_04595.jpg: 256x320 1 BMW M5 Sedan 2010, 13.1ms
image 885/1524 /content/data/images/test/test_04596.jpg: 256x320 1 Mercedes-Benz C-Class Sedan 2012, 13.0ms
image 886/1524 /content/data/images/test/test_04604.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 13.6ms
image 887/1524 /content/data/images/test/test_04610.jpg: 224x320 1 Spyker C8 Convertible 2009, 13.4ms
image 888/1524 /content/data/images/test/test_04624.jpg: 192x320 1 Acura TL Type-S 2008, 13.4ms
image 889/1524 /content/data/images/test/test_04626.jpg: 224x320 1 BMW M6 Convertible 2010, 13.7ms
image 890/1524 /content/data/images/test/test_04630.jpg: 224x320 1 Chevrolet TrailBlazer SS 2009, 18.0ms
image 891/1524 /content/data/images/test/test_04635.jpg: 256x320 1 Plymouth Neon Coupe 1999, 13.1ms
image 892/1524 /content/data/images/test/test_04636.jpg: 224x320 1 Hyundai Veracruz SUV 2012, 13.0ms
image 893/1524 /content/data/images/test/test_04639.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 12.7ms
image 894/1524 /content/data/images/test/test_04650.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 12.5ms
image 895/1524 /content/data/images/test/test_04658.jpg: 160x320 1 BMW M5 Sedan 2010, 13.3ms
image 896/1524 /content/data/images/test/test_04660.jpg: 160x320 1 Honda Odyssey Minivan 2012, 12.6ms
image 897/1524 /content/data/images/test/test_04667.jpg: 224x320 1 Nissan 240SX Coupe 1998, 13.9ms
image 898/1524 /content/data/images/test/test_04676.jpg: 224x320 1 Volkswagen Golf Hatchback 1991, 13.3ms
image 899/1524 /content/data/images/test/test_04684.jpg: 256x320 1 Jeep Patriot SUV 2012, 20.5ms
image 900/1524 /content/data/images/test/test_04703.jpg: 224x320 1 BMW 3 Series Wagon 2012, 16.6ms
image 901/1524 /content/data/images/test/test_04706.jpg: 192x320 1 Hyundai Veloster Hatchback 2012, 13.8ms
image 902/1524 /content/data/images/test/test_04716.jpg: 224x320 1 Toyota 4Runner SUV 2012, 14.0ms
image 903/1524 /content/data/images/test/test_04719.jpg: 224x320 1 Dodge Magnum Wagon 2008, 12.6ms
image 904/1524 /content/data/images/test/test_04730.jpg: 224x320 1 Aston Martin V8 Vantage Convertible 2012, 1 Chevrolet Camaro Convertible 2012, 13.4ms
image 905/1524 /content/data/images/test/test_04731.jpg: 320x320 1 Maybach Landaulet Convertible 2012, 15.0ms
image 906/1524 /content/data/images/test/test_04734.jpg: 256x320 1 Chevrolet Express Van 2007, 1 GMC Savana Van 2012, 12.7ms
image 907/1524 /content/data/images/test/test_04736.jpg: 224x320 1 Chrysler Crossfire Convertible 2008, 12.7ms
image 908/1524 /content/data/images/test/test_04742.jpg: 224x320 1 Volvo 240 Sedan 1993, 13.0ms
image 909/1524 /content/data/images/test/test_04756.jpg: 256x320 1 BMW X5 SUV 2007, 1 Jeep Compass SUV 2012, 14.7ms
image 910/1524 /content/data/images/test/test_04757.jpg: 256x320 1 Acura TL Sedan 2012, 15.1ms
image 911/1524 /content/data/images/test/test_04758.jpg: 224x320 1 BMW 3 Series Sedan 2012, 12.6ms
image 912/1524 /content/data/images/test/test_04764.jpg: 224x320 1 MINI Cooper Roadster Convertible 2012, 11.9ms
image 913/1524 /content/data/images/test/test_04769.jpg: 224x320 1 Chevrolet Traverse SUV 2012, 16.9ms
image 914/1524 /content/data/images/test/test_04771.jpg: 256x320 1 BMW 1 Series Convertible 2012, 14.4ms
image 915/1524 /content/data/images/test/test_04783.jpg: 256x320 1 GMC Terrain SUV 2012, 13.1ms
image 916/1524 /content/data/images/test/test_04796.jpg: 192x320 1 Nissan Leaf Hatchback 2012, 13.2ms
image 917/1524 /content/data/images/test/test_04802.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 1 Ford F-150 Regular Cab 2007, 13.5ms
image 918/1524 /content/data/images/test/test_04805.jpg: 192x320 1 Rolls-Royce Ghost Sedan 2012, 13.2ms
image 919/1524 /content/data/images/test/test_04809.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 13.4ms
image 920/1524 /content/data/images/test/test_04810.jpg: 128x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 18.3ms
image 921/1524 /content/data/images/test/test_04811.jpg: 224x320 1 Mercedes-Benz E-Class Sedan 2012, 15.8ms
image 922/1524 /content/data/images/test/test_04818.jpg: 256x320 1 Volkswagen Golf Hatchback 1991, 13.6ms
image 923/1524 /content/data/images/test/test_04821.jpg: 128x320 1 Mazda Tribute SUV 2011, 13.2ms
image 924/1524 /content/data/images/test/test_04822.jpg: 256x320 1 Bugatti Veyron 16.4 Coupe 2009, 13.6ms
image 925/1524 /content/data/images/test/test_04827.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 14.3ms
image 926/1524 /content/data/images/test/test_04832.jpg: 256x320 1 Suzuki SX4 Hatchback 2012, 17.3ms
image 927/1524 /content/data/images/test/test_04841.jpg: 224x320 1 Mercedes-Benz E-Class Sedan 2012, 13.9ms
image 928/1524 /content/data/images/test/test_04843.jpg: 256x320 1 Dodge Journey SUV 2012, 13.9ms
image 929/1524 /content/data/images/test/test_04846.jpg: 256x320 1 Chrysler Sebring Convertible 2010, 13.2ms
image 930/1524 /content/data/images/test/test_04849.jpg: 288x320 1 Toyota Sequoia SUV 2012, 1 Toyota 4Runner SUV 2012, 14.0ms
image 931/1524 /content/data/images/test/test_04852.jpg: 224x320 1 Toyota Camry Sedan 2012, 15.5ms
image 932/1524 /content/data/images/test/test_04855.jpg: 224x320 1 BMW M5 Sedan 2010, 13.2ms
image 933/1524 /content/data/images/test/test_04860.jpg: 256x320 1 Audi 100 Sedan 1994, 1 Audi 100 Wagon 1994, 14.0ms
image 934/1524 /content/data/images/test/test_04861.jpg: 224x320 1 Nissan Juke Hatchback 2012, 13.2ms
image 935/1524 /content/data/images/test/test_04862.jpg: 256x320 1 Ford Fiesta Sedan 2012, 13.0ms
image 936/1524 /content/data/images/test/test_04863.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 14.7ms
image 937/1524 /content/data/images/test/test_04864.jpg: 128x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 13.4ms
image 938/1524 /content/data/images/test/test_04870.jpg: 224x320 1 BMW X3 SUV 2012, 13.2ms
image 939/1524 /content/data/images/test/test_04874.jpg: 256x320 1 Spyker C8 Coupe 2009, 13.7ms
image 940/1524 /content/data/images/test/test_04879.jpg: 192x320 1 Tesla Model S Sedan 2012, 13.6ms
image 941/1524 /content/data/images/test/test_04890.jpg: 224x320 1 Dodge Charger SRT-8 2009, 13.2ms
image 942/1524 /content/data/images/test/test_04899.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 21.9ms
image 943/1524 /content/data/images/test/test_04901.jpg: 256x320 1 Jeep Wrangler SUV 2012, 8.5ms
image 944/1524 /content/data/images/test/test_04906.jpg: 224x320 1 Cadillac SRX SUV 2012, 8.1ms
image 945/1524 /content/data/images/test/test_04909.jpg: 224x320 1 Suzuki SX4 Hatchback 2012, 7.5ms
image 946/1524 /content/data/images/test/test_04913.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 7.5ms
image 947/1524 /content/data/images/test/test_04916.jpg: 224x320 1 Nissan Leaf Hatchback 2012, 7.4ms
image 948/1524 /content/data/images/test/test_04920.jpg: 224x320 1 Acura RL Sedan 2012, 7.5ms
image 949/1524 /content/data/images/test/test_04939.jpg: 224x320 1 Mercedes-Benz 300-Class Convertible 1993, 11.3ms
image 950/1524 /content/data/images/test/test_04945.jpg: 256x320 1 GMC Savana Van 2012, 7.7ms
image 951/1524 /content/data/images/test/test_04948.jpg: 224x320 1 Chevrolet Camaro Convertible 2012, 7.6ms
image 952/1524 /content/data/images/test/test_04951.jpg: 256x320 1 Acura Integra Type R 2001, 7.4ms
image 953/1524 /content/data/images/test/test_04952.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 7.5ms
image 954/1524 /content/data/images/test/test_04954.jpg: 256x320 1 Ford Expedition EL SUV 2009, 7.3ms
image 955/1524 /content/data/images/test/test_04955.jpg: 320x320 1 Bugatti Veyron 16.4 Coupe 2009, 7.7ms
image 956/1524 /content/data/images/test/test_04958.jpg: 192x320 1 BMW M6 Convertible 2010, 7.4ms
image 957/1524 /content/data/images/test/test_04961.jpg: 224x320 1 Porsche Panamera Sedan 2012, 7.7ms
image 958/1524 /content/data/images/test/test_04982.jpg: 224x320 1 Hyundai Veloster Hatchback 2012, 7.2ms
image 959/1524 /content/data/images/test/test_04991.jpg: 256x320 (no detections), 7.9ms
image 960/1524 /content/data/images/test/test_04993.jpg: 256x320 1 Chevrolet Sonic Sedan 2012, 7.5ms
image 961/1524 /content/data/images/test/test_04994.jpg: 192x320 1 GMC Acadia SUV 2012, 7.3ms
image 962/1524 /content/data/images/test/test_05000.jpg: 256x320 1 Mazda Tribute SUV 2011, 7.5ms
image 963/1524 /content/data/images/test/test_05002.jpg: 224x320 1 Chrysler Aspen SUV 2009, 7.7ms
image 964/1524 /content/data/images/test/test_05006.jpg: 224x320 1 Lamborghini Aventador Coupe 2012, 7.7ms
image 965/1524 /content/data/images/test/test_05008.jpg: 256x320 1 Scion xD Hatchback 2012, 7.6ms
image 966/1524 /content/data/images/test/test_05012.jpg: 256x320 1 GMC Acadia SUV 2012, 7.2ms
image 967/1524 /content/data/images/test/test_05030.jpg: 256x320 1 Suzuki SX4 Hatchback 2012, 7.3ms
image 968/1524 /content/data/images/test/test_05031.jpg: 224x320 1 Audi A5 Coupe 2012, 1 Audi S5 Coupe 2012, 7.4ms
image 969/1524 /content/data/images/test/test_05033.jpg: 192x320 1 Audi S5 Coupe 2012, 7.2ms
image 970/1524 /content/data/images/test/test_05036.jpg: 256x320 1 Volkswagen Beetle Hatchback 2012, 7.3ms
image 971/1524 /content/data/images/test/test_05038.jpg: 256x320 1 Land Rover LR2 SUV 2012, 7.3ms
image 972/1524 /content/data/images/test/test_05050.jpg: 192x320 1 Nissan NV Passenger Van 2012, 7.8ms
image 973/1524 /content/data/images/test/test_05058.jpg: 256x320 1 Ford E-Series Wagon Van 2012, 7.5ms
image 974/1524 /content/data/images/test/test_05059.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.3ms
image 975/1524 /content/data/images/test/test_05064.jpg: 192x320 1 Volvo XC90 SUV 2007, 7.1ms
image 976/1524 /content/data/images/test/test_05074.jpg: 224x320 1 Dodge Charger Sedan 2012, 8.4ms
image 977/1524 /content/data/images/test/test_05075.jpg: 224x320 1 Geo Metro Convertible 1993, 10.2ms
image 978/1524 /content/data/images/test/test_05077.jpg: 256x320 1 Audi 100 Sedan 1994, 7.7ms
image 979/1524 /content/data/images/test/test_05078.jpg: 256x320 1 Volkswagen Golf Hatchback 2012, 7.3ms
image 980/1524 /content/data/images/test/test_05079.jpg: 256x320 1 Ford Freestar Minivan 2007, 7.1ms
image 981/1524 /content/data/images/test/test_05080.jpg: 224x320 1 Aston Martin Virage Coupe 2012, 7.5ms
image 982/1524 /content/data/images/test/test_05092.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 9.2ms
image 983/1524 /content/data/images/test/test_05106.jpg: 256x320 1 Land Rover Range Rover SUV 2012, 8.4ms
image 984/1524 /content/data/images/test/test_05123.jpg: 256x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 7.5ms
image 985/1524 /content/data/images/test/test_05127.jpg: 256x320 1 Lincoln Town Car Sedan 2011, 7.3ms
image 986/1524 /content/data/images/test/test_05130.jpg: 256x320 1 Land Rover LR2 SUV 2012, 9.0ms
image 987/1524 /content/data/images/test/test_05142.jpg: 256x320 1 BMW 1 Series Coupe 2012, 7.2ms
image 988/1524 /content/data/images/test/test_05147.jpg: 224x320 1 BMW X3 SUV 2012, 7.6ms
image 989/1524 /content/data/images/test/test_05149.jpg: 256x320 1 Hyundai Santa Fe SUV 2012, 8.0ms
image 990/1524 /content/data/images/test/test_05153.jpg: 224x320 1 Chrysler Crossfire Convertible 2008, 7.3ms
image 991/1524 /content/data/images/test/test_05162.jpg: 256x320 1 Jeep Patriot SUV 2012, 7.8ms
image 992/1524 /content/data/images/test/test_05171.jpg: 256x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 7.2ms
image 993/1524 /content/data/images/test/test_05182.jpg: 224x320 1 Audi R8 Coupe 2012, 7.6ms
image 994/1524 /content/data/images/test/test_05183.jpg: 224x320 1 Bentley Continental Flying Spur Sedan 2007, 7.3ms
image 995/1524 /content/data/images/test/test_05188.jpg: 256x320 1 Eagle Talon Hatchback 1998, 7.5ms
image 996/1524 /content/data/images/test/test_05190.jpg: 256x320 1 Toyota Corolla Sedan 2012, 7.5ms
image 997/1524 /content/data/images/test/test_05192.jpg: 256x320 1 Mercedes-Benz SL-Class Coupe 2009, 11.6ms
image 998/1524 /content/data/images/test/test_05198.jpg: 224x320 1 Aston Martin V8 Vantage Convertible 2012, 1 Aston Martin Virage Convertible 2012, 7.8ms
image 999/1524 /content/data/images/test/test_05199.jpg: 256x320 1 Bentley Continental Flying Spur Sedan 2007, 7.7ms
image 1000/1524 /content/data/images/test/test_05213.jpg: 256x320 1 Audi A5 Coupe 2012, 7.3ms
image 1001/1524 /content/data/images/test/test_05219.jpg: 256x320 1 BMW 1 Series Convertible 2012, 7.4ms
image 1002/1524 /content/data/images/test/test_05223.jpg: 224x320 1 Dodge Dakota Crew Cab 2010, 8.4ms
image 1003/1524 /content/data/images/test/test_05225.jpg: 256x320 1 Chevrolet Avalanche Crew Cab 2012, 7.6ms
image 1004/1524 /content/data/images/test/test_05227.jpg: 224x320 1 BMW 1 Series Convertible 2012, 1 BMW Z4 Convertible 2012, 8.1ms
image 1005/1524 /content/data/images/test/test_05237.jpg: 224x320 1 Dodge Dakota Crew Cab 2010, 7.4ms
image 1006/1524 /content/data/images/test/test_05239.jpg: 224x320 1 BMW M6 Convertible 2010, 1 BMW Z4 Convertible 2012, 7.4ms
image 1007/1524 /content/data/images/test/test_05245.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 7.9ms
image 1008/1524 /content/data/images/test/test_05246.jpg: 256x320 1 Audi 100 Sedan 1994, 1 Audi 100 Wagon 1994, 7.7ms
image 1009/1524 /content/data/images/test/test_05247.jpg: 224x320 1 Bentley Continental Flying Spur Sedan 2007, 7.7ms
image 1010/1524 /content/data/images/test/test_05251.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 7.4ms
image 1011/1524 /content/data/images/test/test_05258.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 8.4ms
image 1012/1524 /content/data/images/test/test_05264.jpg: 256x320 1 Ferrari 458 Italia Coupe 2012, 7.5ms
image 1013/1524 /content/data/images/test/test_05276.jpg: 256x320 1 Buick Enclave SUV 2012, 7.3ms
image 1014/1524 /content/data/images/test/test_05277.jpg: 224x320 1 Chrysler Sebring Convertible 2010, 7.4ms
image 1015/1524 /content/data/images/test/test_05287.jpg: 192x320 1 Rolls-Royce Phantom Sedan 2012, 7.3ms
image 1016/1524 /content/data/images/test/test_05294.jpg: 256x320 1 Bentley Continental GT Coupe 2012, 7.4ms
image 1017/1524 /content/data/images/test/test_05299.jpg: 224x320 1 Audi TT Hatchback 2011, 7.4ms
image 1018/1524 /content/data/images/test/test_05312.jpg: 192x320 1 Buick Regal GS 2012, 8.3ms
image 1019/1524 /content/data/images/test/test_05317.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 9.5ms
image 1020/1524 /content/data/images/test/test_05320.jpg: 224x320 1 Maybach Landaulet Convertible 2012, 8.0ms
image 1021/1524 /content/data/images/test/test_05321.jpg: 320x320 1 Jeep Compass SUV 2012, 7.7ms
image 1022/1524 /content/data/images/test/test_05324.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 7.6ms
image 1023/1524 /content/data/images/test/test_05326.jpg: 224x320 1 Toyota Camry Sedan 2012, 7.5ms
image 1024/1524 /content/data/images/test/test_05327.jpg: 288x320 1 GMC Canyon Extended Cab 2012, 7.7ms
image 1025/1524 /content/data/images/test/test_05332.jpg: 224x320 1 Porsche Panamera Sedan 2012, 7.4ms
image 1026/1524 /content/data/images/test/test_05339.jpg: 256x320 1 BMW 6 Series Convertible 2007, 7.8ms
image 1027/1524 /content/data/images/test/test_05342.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 11.1ms
image 1028/1524 /content/data/images/test/test_05346.jpg: 160x320 1 Bentley Arnage Sedan 2009, 7.5ms
image 1029/1524 /content/data/images/test/test_05352.jpg: 256x320 1 Dodge Charger SRT-8 2009, 7.8ms
image 1030/1524 /content/data/images/test/test_05363.jpg: 224x320 1 Jeep Patriot SUV 2012, 7.5ms
image 1031/1524 /content/data/images/test/test_05374.jpg: 256x320 1 Jeep Compass SUV 2012, 7.8ms
image 1032/1524 /content/data/images/test/test_05375.jpg: 224x320 1 Lamborghini Aventador Coupe 2012, 7.4ms
image 1033/1524 /content/data/images/test/test_05376.jpg: 224x320 1 Volvo XC90 SUV 2007, 7.3ms
image 1034/1524 /content/data/images/test/test_05394.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 7.4ms
image 1035/1524 /content/data/images/test/test_05398.jpg: 256x320 1 Honda Accord Sedan 2012, 8.1ms
image 1036/1524 /content/data/images/test/test_05406.jpg: 256x320 1 Dodge Durango SUV 2007, 7.3ms
image 1037/1524 /content/data/images/test/test_05420.jpg: 256x320 1 Audi TT Hatchback 2011, 7.7ms
image 1038/1524 /content/data/images/test/test_05434.jpg: 224x320 1 Suzuki Kizashi Sedan 2012, 7.5ms
image 1039/1524 /content/data/images/test/test_05436.jpg: 224x320 1 Nissan Juke Hatchback 2012, 8.3ms
image 1040/1524 /content/data/images/test/test_05444.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 8.9ms
image 1041/1524 /content/data/images/test/test_05450.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 8.5ms
image 1042/1524 /content/data/images/test/test_05454.jpg: 256x320 1 Ford Fiesta Sedan 2012, 8.7ms
image 1043/1524 /content/data/images/test/test_05457.jpg: 256x320 1 Buick Enclave SUV 2012, 7.6ms
image 1044/1524 /content/data/images/test/test_05458.jpg: 224x320 1 Chrysler 300 SRT-8 2010, 1 Rolls-Royce Phantom Sedan 2012, 8.6ms
image 1045/1524 /content/data/images/test/test_05473.jpg: 224x320 1 Rolls-Royce Phantom Sedan 2012, 7.7ms
image 1046/1524 /content/data/images/test/test_05477.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 8.1ms
image 1047/1524 /content/data/images/test/test_05481.jpg: 256x320 1 BMW X3 SUV 2012, 7.4ms
image 1048/1524 /content/data/images/test/test_05484.jpg: 256x320 1 GMC Yukon Hybrid SUV 2012, 7.7ms
image 1049/1524 /content/data/images/test/test_05490.jpg: 160x320 1 Toyota Camry Sedan 2012, 7.4ms
image 1050/1524 /content/data/images/test/test_05497.jpg: 224x320 1 Chevrolet Malibu Sedan 2007, 7.7ms
image 1051/1524 /content/data/images/test/test_05506.jpg: 224x320 1 Aston Martin Virage Convertible 2012, 7.8ms
image 1052/1524 /content/data/images/test/test_05520.jpg: 256x320 1 Dodge Caliber Wagon 2012, 7.7ms
image 1053/1524 /content/data/images/test/test_05531.jpg: 192x320 1 Nissan Leaf Hatchback 2012, 7.6ms
image 1054/1524 /content/data/images/test/test_05546.jpg: 256x320 1 Chrysler Crossfire Convertible 2008, 7.6ms
image 1055/1524 /content/data/images/test/test_05548.jpg: 256x320 1 Ford Edge SUV 2012, 7.3ms
image 1056/1524 /content/data/images/test/test_05569.jpg: 256x320 1 Ford GT Coupe 2006, 7.2ms
image 1057/1524 /content/data/images/test/test_05583.jpg: 288x320 1 Chrysler Town and Country Minivan 2012, 8.0ms
image 1058/1524 /content/data/images/test/test_05587.jpg: 224x320 1 Lamborghini Reventon Coupe 2008, 7.6ms
image 1059/1524 /content/data/images/test/test_05588.jpg: 256x320 1 Chrysler Town and Country Minivan 2012, 7.4ms
image 1060/1524 /content/data/images/test/test_05592.jpg: 256x320 1 Dodge Dakota Crew Cab 2010, 7.5ms
image 1061/1524 /content/data/images/test/test_05610.jpg: 224x320 1 Mercedes-Benz Sprinter Van 2012, 7.6ms
image 1062/1524 /content/data/images/test/test_05626.jpg: 192x320 1 Land Rover Range Rover SUV 2012, 7.5ms
image 1063/1524 /content/data/images/test/test_05628.jpg: 256x320 1 Ford F-150 Regular Cab 2012, 7.9ms
image 1064/1524 /content/data/images/test/test_05640.jpg: 224x320 1 Audi TTS Coupe 2012, 10.8ms
image 1065/1524 /content/data/images/test/test_05649.jpg: 256x320 1 Dodge Magnum Wagon 2008, 7.7ms
image 1066/1524 /content/data/images/test/test_05652.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 GMC Canyon Extended Cab 2012, 8.6ms
image 1067/1524 /content/data/images/test/test_05663.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 7.5ms
image 1068/1524 /content/data/images/test/test_05667.jpg: 256x320 1 Acura Integra Type R 2001, 7.3ms
image 1069/1524 /content/data/images/test/test_05668.jpg: 256x320 1 Hyundai Sonata Sedan 2012, 7.5ms
image 1070/1524 /content/data/images/test/test_05671.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 7.4ms
image 1071/1524 /content/data/images/test/test_05674.jpg: 256x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 1 Dodge Ram Pickup 3500 Quad Cab 2009, 7.2ms
image 1072/1524 /content/data/images/test/test_05680.jpg: 192x320 1 GMC Yukon Hybrid SUV 2012, 7.6ms
image 1073/1524 /content/data/images/test/test_05681.jpg: 224x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 8.0ms
image 1074/1524 /content/data/images/test/test_05685.jpg: 256x320 1 BMW Z4 Convertible 2012, 8.6ms
image 1075/1524 /content/data/images/test/test_05693.jpg: 224x320 1 Dodge Durango SUV 2012, 8.4ms
image 1076/1524 /content/data/images/test/test_05697.jpg: 224x320 1 Buick Regal GS 2012, 7.5ms
image 1077/1524 /content/data/images/test/test_05702.jpg: 256x320 1 Isuzu Ascender SUV 2008, 8.1ms
image 1078/1524 /content/data/images/test/test_05711.jpg: 192x320 1 Chevrolet Sonic Sedan 2012, 7.2ms
image 1079/1524 /content/data/images/test/test_05714.jpg: 256x320 1 Ford Mustang Convertible 2007, 7.5ms
image 1080/1524 /content/data/images/test/test_05723.jpg: 224x320 1 smart fortwo Convertible 2012, 8.5ms
image 1081/1524 /content/data/images/test/test_05725.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 8.3ms
image 1082/1524 /content/data/images/test/test_05730.jpg: 256x320 1 Ford GT Coupe 2006, 7.9ms
image 1083/1524 /content/data/images/test/test_05745.jpg: 256x320 1 Hyundai Accent Sedan 2012, 8.7ms
image 1084/1524 /content/data/images/test/test_05760.jpg: 256x320 1 Honda Accord Sedan 2012, 8.7ms
image 1085/1524 /content/data/images/test/test_05763.jpg: 256x320 1 Chevrolet TrailBlazer SS 2009, 9.1ms
image 1086/1524 /content/data/images/test/test_05764.jpg: 256x320 1 Chevrolet Malibu Sedan 2007, 7.9ms
image 1087/1524 /content/data/images/test/test_05771.jpg: 224x320 1 Mazda Tribute SUV 2011, 8.1ms
image 1088/1524 /content/data/images/test/test_05773.jpg: 224x320 1 GMC Yukon Hybrid SUV 2012, 8.2ms
image 1089/1524 /content/data/images/test/test_05775.jpg: 192x320 1 Ferrari 458 Italia Coupe 2012, 8.2ms
image 1090/1524 /content/data/images/test/test_05786.jpg: 128x320 1 Mercedes-Benz S-Class Sedan 2012, 8.0ms
image 1091/1524 /content/data/images/test/test_05794.jpg: 256x320 1 Dodge Durango SUV 2007, 8.6ms
image 1092/1524 /content/data/images/test/test_05806.jpg: 256x320 1 Toyota Camry Sedan 2012, 11.5ms
image 1093/1524 /content/data/images/test/test_05821.jpg: 256x320 1 Chrysler Town and Country Minivan 2012, 7.9ms
image 1094/1524 /content/data/images/test/test_05823.jpg: 224x320 1 Audi A5 Coupe 2012, 1 Audi S5 Coupe 2012, 8.2ms
image 1095/1524 /content/data/images/test/test_05826.jpg: 256x320 1 Lamborghini Diablo Coupe 2001, 8.6ms
image 1096/1524 /content/data/images/test/test_05829.jpg: 224x320 1 Jeep Liberty SUV 2012, 7.5ms
image 1097/1524 /content/data/images/test/test_05830.jpg: 256x320 1 Audi RS 4 Convertible 2008, 7.7ms
image 1098/1524 /content/data/images/test/test_05839.jpg: 224x320 1 Scion xD Hatchback 2012, 7.8ms
image 1099/1524 /content/data/images/test/test_05842.jpg: 192x320 1 Audi S4 Sedan 2012, 7.5ms
image 1100/1524 /content/data/images/test/test_05843.jpg: 192x320 1 Hyundai Sonata Sedan 2012, 7.0ms
image 1101/1524 /content/data/images/test/test_05846.jpg: 256x320 1 Isuzu Ascender SUV 2008, 7.9ms
image 1102/1524 /content/data/images/test/test_05847.jpg: 256x320 1 Geo Metro Convertible 1993, 8.3ms
image 1103/1524 /content/data/images/test/test_05851.jpg: 224x320 1 Audi TT RS Coupe 2012, 8.9ms
image 1104/1524 /content/data/images/test/test_05863.jpg: 256x320 1 Buick Rainier SUV 2007, 7.6ms
image 1105/1524 /content/data/images/test/test_05865.jpg: 192x320 1 FIAT 500 Convertible 2012, 7.4ms
image 1106/1524 /content/data/images/test/test_05875.jpg: 224x320 1 Audi S5 Convertible 2012, 7.5ms
image 1107/1524 /content/data/images/test/test_05877.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 1 GMC Terrain SUV 2012, 7.9ms
image 1108/1524 /content/data/images/test/test_05881.jpg: 256x320 1 Audi S4 Sedan 2007, 7.4ms
image 1109/1524 /content/data/images/test/test_05894.jpg: 256x320 1 Mercedes-Benz Sprinter Van 2012, 8.1ms
image 1110/1524 /content/data/images/test/test_05895.jpg: 256x320 1 Audi R8 Coupe 2012, 7.9ms
image 1111/1524 /content/data/images/test/test_05897.jpg: 256x320 1 Audi RS 4 Convertible 2008, 7.3ms
image 1112/1524 /content/data/images/test/test_05918.jpg: 256x320 1 Chevrolet Silverado 1500 Extended Cab 2012, 7.2ms
image 1113/1524 /content/data/images/test/test_05922.jpg: 192x320 1 Ford Edge SUV 2012, 7.1ms
image 1114/1524 /content/data/images/test/test_05926.jpg: 256x320 1 Hyundai Elantra Sedan 2007, 8.8ms
image 1115/1524 /content/data/images/test/test_05933.jpg: 256x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 7.3ms
image 1116/1524 /content/data/images/test/test_05935.jpg: 256x320 1 Audi 100 Sedan 1994, 8.2ms
image 1117/1524 /content/data/images/test/test_05937.jpg: 256x320 1 Spyker C8 Convertible 2009, 7.6ms
image 1118/1524 /content/data/images/test/test_05947.jpg: 224x320 1 Ferrari 458 Italia Coupe 2012, 7.6ms
image 1119/1524 /content/data/images/test/test_05948.jpg: 256x320 1 Mazda Tribute SUV 2011, 7.7ms
image 1120/1524 /content/data/images/test/test_05949.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 1 GMC Savana Van 2012, 8.9ms
image 1121/1524 /content/data/images/test/test_05952.jpg: 320x320 1 Hyundai Accent Sedan 2012, 8.0ms
image 1122/1524 /content/data/images/test/test_05960.jpg: 256x320 1 Buick Enclave SUV 2012, 7.5ms
image 1123/1524 /content/data/images/test/test_05962.jpg: 224x320 1 Hyundai Genesis Sedan 2012, 7.3ms
image 1124/1524 /content/data/images/test/test_05978.jpg: 224x320 1 Ferrari FF Coupe 2012, 7.4ms
image 1125/1524 /content/data/images/test/test_05988.jpg: 224x320 1 Land Rover Range Rover SUV 2012, 7.1ms
image 1126/1524 /content/data/images/test/test_05989.jpg: 224x320 1 Honda Odyssey Minivan 2007, 7.4ms
image 1127/1524 /content/data/images/test/test_05997.jpg: 224x320 1 Tesla Model S Sedan 2012, 7.2ms
image 1128/1524 /content/data/images/test/test_06000.jpg: 224x320 1 Toyota 4Runner SUV 2012, 11.7ms
image 1129/1524 /content/data/images/test/test_06001.jpg: 224x320 1 MINI Cooper Roadster Convertible 2012, 7.6ms
image 1130/1524 /content/data/images/test/test_06007.jpg: 256x320 1 GMC Savana Van 2012, 7.8ms
image 1131/1524 /content/data/images/test/test_06008.jpg: 224x320 1 Mercedes-Benz S-Class Sedan 2012, 10.1ms
image 1132/1524 /content/data/images/test/test_06012.jpg: 192x320 1 Buick Verano Sedan 2012, 8.2ms
image 1133/1524 /content/data/images/test/test_06017.jpg: 224x320 1 McLaren MP4-12C Coupe 2012, 7.7ms
image 1134/1524 /content/data/images/test/test_06019.jpg: 256x320 1 Ford Expedition EL SUV 2009, 7.6ms
image 1135/1524 /content/data/images/test/test_06020.jpg: 160x320 1 Infiniti QX56 SUV 2011, 7.6ms
image 1136/1524 /content/data/images/test/test_06021.jpg: 256x320 1 Suzuki Kizashi Sedan 2012, 8.0ms
image 1137/1524 /content/data/images/test/test_06036.jpg: 256x320 1 Dodge Caliber Wagon 2007, 7.6ms
image 1138/1524 /content/data/images/test/test_06049.jpg: 224x320 1 Mercedes-Benz SL-Class Coupe 2009, 8.1ms
image 1139/1524 /content/data/images/test/test_06057.jpg: 288x320 1 Acura TL Sedan 2012, 8.4ms
image 1140/1524 /content/data/images/test/test_06062.jpg: 224x320 1 Chevrolet Malibu Hybrid Sedan 2010, 8.2ms
image 1141/1524 /content/data/images/test/test_06074.jpg: 160x320 1 Chevrolet Cobalt SS 2010, 8.1ms
image 1142/1524 /content/data/images/test/test_06077.jpg: 256x320 1 Jeep Patriot SUV 2012, 8.2ms
image 1143/1524 /content/data/images/test/test_06090.jpg: 256x320 1 Scion xD Hatchback 2012, 7.8ms
image 1144/1524 /content/data/images/test/test_06093.jpg: 256x320 1 Audi A5 Coupe 2012, 7.7ms
image 1145/1524 /content/data/images/test/test_06100.jpg: 256x320 1 BMW M3 Coupe 2012, 7.7ms
image 1146/1524 /content/data/images/test/test_06101.jpg: 288x320 1 Rolls-Royce Phantom Drophead Coupe Convertible 2012, 7.7ms
image 1147/1524 /content/data/images/test/test_06108.jpg: 192x320 1 Ferrari 458 Italia Coupe 2012, 7.3ms
image 1148/1524 /content/data/images/test/test_06109.jpg: 224x320 1 Chevrolet Corvette Convertible 2012, 8.2ms
image 1149/1524 /content/data/images/test/test_06114.jpg: 256x320 1 Audi S5 Coupe 2012, 7.6ms
image 1150/1524 /content/data/images/test/test_06118.jpg: 224x320 1 Volkswagen Beetle Hatchback 2012, 7.4ms
image 1151/1524 /content/data/images/test/test_06122.jpg: 256x320 1 BMW X6 SUV 2012, 7.7ms
image 1152/1524 /content/data/images/test/test_06135.jpg: 192x320 1 Audi A5 Coupe 2012, 7.9ms
image 1153/1524 /content/data/images/test/test_06136.jpg: 256x320 1 Toyota 4Runner SUV 2012, 12.9ms
image 1154/1524 /content/data/images/test/test_06138.jpg: 256x320 1 Chrysler Aspen SUV 2009, 7.6ms
image 1155/1524 /content/data/images/test/test_06141.jpg: 224x320 1 Chrysler Aspen SUV 2009, 7.6ms
image 1156/1524 /content/data/images/test/test_06150.jpg: 256x320 1 Bentley Continental Flying Spur Sedan 2007, 7.6ms
image 1157/1524 /content/data/images/test/test_06152.jpg: 256x320 1 Chevrolet TrailBlazer SS 2009, 9.3ms
image 1158/1524 /content/data/images/test/test_06154.jpg: 224x320 1 Buick Rainier SUV 2007, 8.0ms
image 1159/1524 /content/data/images/test/test_06169.jpg: 224x320 (no detections), 7.4ms
image 1160/1524 /content/data/images/test/test_06171.jpg: 256x320 1 Eagle Talon Hatchback 1998, 8.3ms
image 1161/1524 /content/data/images/test/test_06172.jpg: 224x320 1 Audi TT RS Coupe 2012, 7.9ms
image 1162/1524 /content/data/images/test/test_06173.jpg: 256x320 1 Infiniti G Coupe IPL 2012, 7.9ms
image 1163/1524 /content/data/images/test/test_06177.jpg: 256x320 1 Chrysler Crossfire Convertible 2008, 7.2ms
image 1164/1524 /content/data/images/test/test_06181.jpg: 256x320 1 Acura Integra Type R 2001, 7.4ms
image 1165/1524 /content/data/images/test/test_06188.jpg: 224x320 1 Audi S5 Convertible 2012, 8.1ms
image 1166/1524 /content/data/images/test/test_06192.jpg: 224x320 1 BMW ActiveHybrid 5 Sedan 2012, 7.4ms
image 1167/1524 /content/data/images/test/test_06193.jpg: 224x320 1 Aston Martin Virage Coupe 2012, 10.5ms
image 1168/1524 /content/data/images/test/test_06194.jpg: 160x320 1 Mercedes-Benz S-Class Sedan 2012, 8.3ms
image 1169/1524 /content/data/images/test/test_06200.jpg: 224x320 1 Mercedes-Benz SL-Class Coupe 2009, 8.1ms
image 1170/1524 /content/data/images/test/test_06201.jpg: 224x320 1 Land Rover LR2 SUV 2012, 12.9ms
image 1171/1524 /content/data/images/test/test_06208.jpg: 256x320 1 HUMMER H3T Crew Cab 2010, 8.5ms
image 1172/1524 /content/data/images/test/test_06210.jpg: 224x320 1 Spyker C8 Convertible 2009, 1 Spyker C8 Coupe 2009, 8.7ms
image 1173/1524 /content/data/images/test/test_06217.jpg: 256x320 1 Mazda Tribute SUV 2011, 8.3ms
image 1174/1524 /content/data/images/test/test_06218.jpg: 192x320 1 Ford F-450 Super Duty Crew Cab 2012, 8.2ms
image 1175/1524 /content/data/images/test/test_06219.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 8.7ms
image 1176/1524 /content/data/images/test/test_06227.jpg: 224x320 1 HUMMER H3T Crew Cab 2010, 9.1ms
image 1177/1524 /content/data/images/test/test_06230.jpg: 224x320 1 Ferrari FF Coupe 2012, 8.4ms
image 1178/1524 /content/data/images/test/test_06233.jpg: 224x320 1 Ford F-450 Super Duty Crew Cab 2012, 8.0ms
image 1179/1524 /content/data/images/test/test_06246.jpg: 192x320 1 Buick Rainier SUV 2007, 7.8ms
image 1180/1524 /content/data/images/test/test_06250.jpg: 224x320 1 Acura RL Sedan 2012, 8.7ms
image 1181/1524 /content/data/images/test/test_06253.jpg: 256x320 1 Ford F-150 Regular Cab 2007, 8.4ms
image 1182/1524 /content/data/images/test/test_06254.jpg: 256x320 1 Rolls-Royce Phantom Sedan 2012, 7.5ms
image 1183/1524 /content/data/images/test/test_06260.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.6ms
image 1184/1524 /content/data/images/test/test_06262.jpg: 224x320 1 BMW 1 Series Coupe 2012, 7.9ms
image 1185/1524 /content/data/images/test/test_06271.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 8.4ms
image 1186/1524 /content/data/images/test/test_06284.jpg: 192x320 1 Eagle Talon Hatchback 1998, 8.2ms
image 1187/1524 /content/data/images/test/test_06288.jpg: 224x320 1 Dodge Journey SUV 2012, 7.9ms
image 1188/1524 /content/data/images/test/test_06289.jpg: 256x320 1 Ford Focus Sedan 2007, 7.9ms
image 1189/1524 /content/data/images/test/test_06293.jpg: 256x320 1 Audi S5 Convertible 2012, 7.6ms
image 1190/1524 /content/data/images/test/test_06294.jpg: 256x320 1 Suzuki Aerio Sedan 2007, 7.9ms
image 1191/1524 /content/data/images/test/test_06298.jpg: 224x320 1 Ferrari FF Coupe 2012, 1 Fisker Karma Sedan 2012, 7.5ms
image 1192/1524 /content/data/images/test/test_06305.jpg: 256x320 1 HUMMER H2 SUT Crew Cab 2009, 7.6ms
image 1193/1524 /content/data/images/test/test_06312.jpg: 320x320 1 Ford GT Coupe 2006, 7.9ms
image 1194/1524 /content/data/images/test/test_06316.jpg: 256x320 1 Hyundai Genesis Sedan 2012, 7.7ms
image 1195/1524 /content/data/images/test/test_06319.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 9.5ms
image 1196/1524 /content/data/images/test/test_06323.jpg: 256x320 1 Bentley Continental Flying Spur Sedan 2007, 7.7ms
image 1197/1524 /content/data/images/test/test_06334.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 10.9ms
image 1198/1524 /content/data/images/test/test_06336.jpg: 256x320 1 Acura TL Type-S 2008, 7.4ms
image 1199/1524 /content/data/images/test/test_06343.jpg: 224x320 1 Mercedes-Benz E-Class Sedan 2012, 7.7ms
image 1200/1524 /content/data/images/test/test_06348.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 7.6ms
image 1201/1524 /content/data/images/test/test_06349.jpg: 256x320 1 Plymouth Neon Coupe 1999, 9.6ms
image 1202/1524 /content/data/images/test/test_06350.jpg: 256x320 1 Dodge Caravan Minivan 1997, 7.5ms
image 1203/1524 /content/data/images/test/test_06355.jpg: 256x320 1 Volvo 240 Sedan 1993, 7.7ms
image 1204/1524 /content/data/images/test/test_06362.jpg: 256x320 1 Jeep Liberty SUV 2012, 7.8ms
image 1205/1524 /content/data/images/test/test_06363.jpg: 256x320 1 Honda Accord Sedan 2012, 9.1ms
image 1206/1524 /content/data/images/test/test_06367.jpg: 256x320 1 Volkswagen Golf Hatchback 2012, 7.9ms
image 1207/1524 /content/data/images/test/test_06375.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 11.3ms
image 1208/1524 /content/data/images/test/test_06381.jpg: 256x320 1 Hyundai Sonata Sedan 2012, 7.5ms
image 1209/1524 /content/data/images/test/test_06386.jpg: 224x320 1 Nissan Leaf Hatchback 2012, 8.5ms
image 1210/1524 /content/data/images/test/test_06390.jpg: 256x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 1 Dodge Dakota Club Cab 2007, 8.3ms
image 1211/1524 /content/data/images/test/test_06394.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 7.5ms
image 1212/1524 /content/data/images/test/test_06395.jpg: 288x320 1 BMW X5 SUV 2007, 8.1ms
image 1213/1524 /content/data/images/test/test_06412.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 8.0ms
image 1214/1524 /content/data/images/test/test_06423.jpg: 256x320 1 Chevrolet Sonic Sedan 2012, 7.3ms
image 1215/1524 /content/data/images/test/test_06430.jpg: 256x320 1 Dodge Ram Pickup 3500 Quad Cab 2009, 9.0ms
image 1216/1524 /content/data/images/test/test_06432.jpg: 256x320 1 Chevrolet Camaro Convertible 2012, 7.9ms
image 1217/1524 /content/data/images/test/test_06435.jpg: 256x320 1 Mitsubishi Lancer Sedan 2012, 7.8ms
image 1218/1524 /content/data/images/test/test_06442.jpg: 224x320 1 Geo Metro Convertible 1993, 11.2ms
image 1219/1524 /content/data/images/test/test_06449.jpg: 224x320 1 Bentley Continental GT Coupe 2012, 7.5ms
image 1220/1524 /content/data/images/test/test_06454.jpg: 224x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 7.5ms
image 1221/1524 /content/data/images/test/test_06456.jpg: 256x320 1 GMC Canyon Extended Cab 2012, 7.6ms
image 1222/1524 /content/data/images/test/test_06458.jpg: 256x320 1 Chevrolet Silverado 1500 Extended Cab 2012, 7.5ms
image 1223/1524 /content/data/images/test/test_06471.jpg: 256x320 1 Chevrolet Malibu Hybrid Sedan 2010, 7.4ms
image 1224/1524 /content/data/images/test/test_06477.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 1 Mercedes-Benz E-Class Sedan 2012, 7.9ms
image 1225/1524 /content/data/images/test/test_06478.jpg: 224x320 1 Acura TL Sedan 2012, 7.9ms
image 1226/1524 /content/data/images/test/test_06479.jpg: 160x320 1 smart fortwo Convertible 2012, 7.5ms
image 1227/1524 /content/data/images/test/test_06486.jpg: 256x320 1 Chevrolet Impala Sedan 2007, 7.6ms
image 1228/1524 /content/data/images/test/test_06491.jpg: 256x320 1 Ford Focus Sedan 2007, 7.3ms
image 1229/1524 /content/data/images/test/test_06496.jpg: 224x320 1 Bentley Continental Flying Spur Sedan 2007, 7.6ms
image 1230/1524 /content/data/images/test/test_06502.jpg: 256x320 1 Ford Edge SUV 2012, 7.5ms
image 1231/1524 /content/data/images/test/test_06512.jpg: 256x320 1 Chevrolet Silverado 1500 Hybrid Crew Cab 2012, 1 Chevrolet Silverado 1500 Extended Cab 2012, 7.5ms
image 1232/1524 /content/data/images/test/test_06523.jpg: 192x320 1 Audi TT RS Coupe 2012, 8.9ms
image 1233/1524 /content/data/images/test/test_06525.jpg: 256x320 1 GMC Savana Van 2012, 10.9ms
image 1234/1524 /content/data/images/test/test_06526.jpg: 224x320 1 Bentley Mulsanne Sedan 2011, 8.2ms
image 1235/1524 /content/data/images/test/test_06527.jpg: 256x320 1 Honda Odyssey Minivan 2012, 7.4ms
image 1236/1524 /content/data/images/test/test_06536.jpg: 256x320 1 Mercedes-Benz C-Class Sedan 2012, 10.1ms
image 1237/1524 /content/data/images/test/test_06537.jpg: 160x320 1 Volvo 240 Sedan 1993, 7.4ms
image 1238/1524 /content/data/images/test/test_06549.jpg: 192x320 1 HUMMER H2 SUT Crew Cab 2009, 11.4ms
image 1239/1524 /content/data/images/test/test_06570.jpg: 224x320 1 Mazda Tribute SUV 2011, 7.8ms
image 1240/1524 /content/data/images/test/test_06582.jpg: 256x320 2 BMW 3 Series Sedan 2012s, 8.3ms
image 1241/1524 /content/data/images/test/test_06585.jpg: 256x320 1 BMW M5 Sedan 2010, 7.2ms
image 1242/1524 /content/data/images/test/test_06589.jpg: 256x320 1 Bentley Mulsanne Sedan 2011, 7.7ms
image 1243/1524 /content/data/images/test/test_06596.jpg: 192x320 1 Hyundai Elantra Touring Hatchback 2012, 1 Volkswagen Golf Hatchback 2012, 7.1ms
image 1244/1524 /content/data/images/test/test_06598.jpg: 192x320 1 Dodge Durango SUV 2007, 7.1ms
image 1245/1524 /content/data/images/test/test_06600.jpg: 256x320 1 Jeep Wrangler SUV 2012, 7.4ms
image 1246/1524 /content/data/images/test/test_06602.jpg: 224x320 1 Chrysler 300 SRT-8 2010, 7.4ms
image 1247/1524 /content/data/images/test/test_06603.jpg: 224x320 1 Dodge Charger SRT-8 2009, 7.1ms
image 1248/1524 /content/data/images/test/test_06604.jpg: 224x320 1 Dodge Caliber Wagon 2012, 7.2ms
image 1249/1524 /content/data/images/test/test_06606.jpg: 256x320 1 Lamborghini Aventador Coupe 2012, 8.3ms
image 1250/1524 /content/data/images/test/test_06610.jpg: 256x320 1 Audi 100 Wagon 1994, 7.3ms
image 1251/1524 /content/data/images/test/test_06617.jpg: 192x320 1 Jaguar XK XKR 2012, 7.5ms
image 1252/1524 /content/data/images/test/test_06620.jpg: 256x320 1 Suzuki Aerio Sedan 2007, 9.1ms
image 1253/1524 /content/data/images/test/test_06626.jpg: 256x320 1 BMW X5 SUV 2007, 7.8ms
image 1254/1524 /content/data/images/test/test_06627.jpg: 224x320 1 Lamborghini Gallardo LP 570-4 Superleggera 2012, 8.1ms
image 1255/1524 /content/data/images/test/test_06629.jpg: 256x320 1 Toyota Corolla Sedan 2012, 8.8ms
image 1256/1524 /content/data/images/test/test_06635.jpg: 224x320 1 Hyundai Sonata Hybrid Sedan 2012, 7.7ms
image 1257/1524 /content/data/images/test/test_06641.jpg: 256x320 1 Bentley Continental Supersports Conv. Convertible 2012, 10.4ms
image 1258/1524 /content/data/images/test/test_06646.jpg: 224x320 1 BMW M3 Coupe 2012, 8.0ms
image 1259/1524 /content/data/images/test/test_06649.jpg: 256x320 1 Chevrolet TrailBlazer SS 2009, 7.6ms
image 1260/1524 /content/data/images/test/test_06653.jpg: 160x320 1 GMC Acadia SUV 2012, 7.2ms
image 1261/1524 /content/data/images/test/test_06654.jpg: 160x320 1 Chevrolet Malibu Sedan 2007, 7.6ms
image 1262/1524 /content/data/images/test/test_06662.jpg: 224x320 1 Infiniti G Coupe IPL 2012, 7.4ms
image 1263/1524 /content/data/images/test/test_06664.jpg: 256x320 1 Chevrolet Malibu Hybrid Sedan 2010, 7.3ms
image 1264/1524 /content/data/images/test/test_06669.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 7.2ms
image 1265/1524 /content/data/images/test/test_06671.jpg: 160x320 1 Toyota Camry Sedan 2012, 7.4ms
image 1266/1524 /content/data/images/test/test_06672.jpg: 256x320 1 Dodge Ram Pickup 3500 Crew Cab 2010, 7.5ms
image 1267/1524 /content/data/images/test/test_06681.jpg: 288x320 1 Audi S6 Sedan 2011, 7.7ms
image 1268/1524 /content/data/images/test/test_06684.jpg: 256x320 1 Cadillac Escalade EXT Crew Cab 2007, 1 Dodge Dakota Crew Cab 2010, 7.5ms
image 1269/1524 /content/data/images/test/test_06689.jpg: 256x320 1 Hyundai Sonata Hybrid Sedan 2012, 8.9ms
image 1270/1524 /content/data/images/test/test_06693.jpg: 224x320 1 Spyker C8 Convertible 2009, 7.7ms
image 1271/1524 /content/data/images/test/test_06698.jpg: 224x320 1 Audi S4 Sedan 2007, 7.3ms
image 1272/1524 /content/data/images/test/test_06707.jpg: 224x320 1 Lamborghini Diablo Coupe 2001, 7.8ms
image 1273/1524 /content/data/images/test/test_06721.jpg: 256x320 1 HUMMER H3T Crew Cab 2010, 13.8ms
image 1274/1524 /content/data/images/test/test_06723.jpg: 224x320 1 Hyundai Azera Sedan 2012, 7.6ms
image 1275/1524 /content/data/images/test/test_06729.jpg: 256x320 1 Audi TTS Coupe 2012, 7.6ms
image 1276/1524 /content/data/images/test/test_06734.jpg: 256x320 1 Eagle Talon Hatchback 1998, 8.4ms
image 1277/1524 /content/data/images/test/test_06736.jpg: 160x320 1 Chrysler PT Cruiser Convertible 2008, 7.3ms
image 1278/1524 /content/data/images/test/test_06737.jpg: 224x320 1 Maybach Landaulet Convertible 2012, 7.7ms
image 1279/1524 /content/data/images/test/test_06740.jpg: 256x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 1 GMC Savana Van 2012, 7.5ms
image 1280/1524 /content/data/images/test/test_06743.jpg: 256x320 1 Chrysler Crossfire Convertible 2008, 7.3ms
image 1281/1524 /content/data/images/test/test_06746.jpg: 256x320 1 Lamborghini Diablo Coupe 2001, 7.1ms
image 1282/1524 /content/data/images/test/test_06755.jpg: 256x320 1 Buick Enclave SUV 2012, 9.1ms
image 1283/1524 /content/data/images/test/test_06758.jpg: 224x320 1 BMW 3 Series Wagon 2012, 7.4ms
image 1284/1524 /content/data/images/test/test_06764.jpg: 192x320 1 Fisker Karma Sedan 2012, 11.4ms
image 1285/1524 /content/data/images/test/test_06766.jpg: 224x320 1 Rolls-Royce Ghost Sedan 2012, 7.6ms
image 1286/1524 /content/data/images/test/test_06770.jpg: 256x320 1 Audi V8 Sedan 1994, 7.6ms
image 1287/1524 /content/data/images/test/test_06772.jpg: 224x320 1 Lamborghini Aventador Coupe 2012, 7.4ms
image 1288/1524 /content/data/images/test/test_06779.jpg: 224x320 1 BMW M3 Coupe 2012, 7.3ms
image 1289/1524 /content/data/images/test/test_06782.jpg: 256x320 1 Jeep Liberty SUV 2012, 7.5ms
image 1290/1524 /content/data/images/test/test_06788.jpg: 256x320 1 Toyota Camry Sedan 2012, 7.4ms
image 1291/1524 /content/data/images/test/test_06803.jpg: 256x320 1 Acura TL Type-S 2008, 7.6ms
image 1292/1524 /content/data/images/test/test_06804.jpg: 256x320 1 Toyota Corolla Sedan 2012, 7.4ms
image 1293/1524 /content/data/images/test/test_06805.jpg: 320x320 1 Hyundai Sonata Hybrid Sedan 2012, 8.9ms
image 1294/1524 /content/data/images/test/test_06806.jpg: 224x320 1 Bentley Arnage Sedan 2009, 7.6ms
image 1295/1524 /content/data/images/test/test_06809.jpg: 256x320 1 Lamborghini Reventon Coupe 2008, 8.2ms
image 1296/1524 /content/data/images/test/test_06819.jpg: 224x320 1 Chevrolet Monte Carlo Coupe 2007, 7.5ms
image 1297/1524 /content/data/images/test/test_06822.jpg: 256x320 1 Nissan 240SX Coupe 1998, 7.3ms
image 1298/1524 /content/data/images/test/test_06826.jpg: 256x320 1 Ford F-150 Regular Cab 2012, 7.5ms
image 1299/1524 /content/data/images/test/test_06828.jpg: 256x320 1 Ram C-V Cargo Van Minivan 2012, 7.5ms
image 1300/1524 /content/data/images/test/test_06845.jpg: 224x320 1 Suzuki SX4 Sedan 2012, 9.3ms
image 1301/1524 /content/data/images/test/test_06852.jpg: 256x320 1 Acura Integra Type R 2001, 7.6ms
image 1302/1524 /content/data/images/test/test_06856.jpg: 224x320 1 FIAT 500 Convertible 2012, 7.8ms
image 1303/1524 /content/data/images/test/test_06859.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 11.6ms
image 1304/1524 /content/data/images/test/test_06861.jpg: 224x320 1 Jeep Grand Cherokee SUV 2012, 7.4ms
image 1305/1524 /content/data/images/test/test_06867.jpg: 256x320 1 Dodge Dakota Crew Cab 2010, 7.5ms
image 1306/1524 /content/data/images/test/test_06871.jpg: 256x320 1 Acura TL Type-S 2008, 7.2ms
image 1307/1524 /content/data/images/test/test_06872.jpg: 256x320 1 Mercedes-Benz 300-Class Convertible 1993, 7.6ms
image 1308/1524 /content/data/images/test/test_06882.jpg: 256x320 1 BMW M6 Convertible 2010, 7.2ms
image 1309/1524 /content/data/images/test/test_06884.jpg: 256x320 1 BMW X5 SUV 2007, 7.3ms
image 1310/1524 /content/data/images/test/test_06889.jpg: 224x320 1 Jeep Patriot SUV 2012, 7.4ms
image 1311/1524 /content/data/images/test/test_06901.jpg: 256x320 1 Chevrolet HHR SS 2010, 7.6ms
image 1312/1524 /content/data/images/test/test_06907.jpg: 256x320 1 Hyundai Sonata Hybrid Sedan 2012, 7.1ms
image 1313/1524 /content/data/images/test/test_06912.jpg: 256x320 1 Audi V8 Sedan 1994, 8.8ms
image 1314/1524 /content/data/images/test/test_06914.jpg: 224x320 1 Ferrari FF Coupe 2012, 12.5ms
image 1315/1524 /content/data/images/test/test_06916.jpg: 256x320 1 Plymouth Neon Coupe 1999, 7.6ms
image 1316/1524 /content/data/images/test/test_06917.jpg: 192x320 1 Chevrolet Sonic Sedan 2012, 7.2ms
image 1317/1524 /content/data/images/test/test_06925.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 7.4ms
image 1318/1524 /content/data/images/test/test_06927.jpg: 256x320 1 Land Rover LR2 SUV 2012, 7.9ms
image 1319/1524 /content/data/images/test/test_06928.jpg: 256x320 1 Lamborghini Reventon Coupe 2008, 7.2ms
image 1320/1524 /content/data/images/test/test_06931.jpg: 160x320 1 Hyundai Elantra Touring Hatchback 2012, 7.5ms
image 1321/1524 /content/data/images/test/test_06938.jpg: 224x320 1 Tesla Model S Sedan 2012, 10.6ms
image 1322/1524 /content/data/images/test/test_06960.jpg: 224x320 1 Nissan Leaf Hatchback 2012, 7.6ms
image 1323/1524 /content/data/images/test/test_06973.jpg: 256x320 1 Honda Odyssey Minivan 2012, 7.4ms
image 1324/1524 /content/data/images/test/test_06980.jpg: 224x320 1 Ford Mustang Convertible 2007, 7.6ms
image 1325/1524 /content/data/images/test/test_06987.jpg: 256x320 1 GMC Savana Van 2012, 8.1ms
image 1326/1524 /content/data/images/test/test_06998.jpg: 256x320 1 Cadillac SRX SUV 2012, 7.6ms
image 1327/1524 /content/data/images/test/test_07005.jpg: 256x320 1 Chevrolet Silverado 1500 Classic Extended Cab 2007, 7.3ms
image 1328/1524 /content/data/images/test/test_07008.jpg: 256x320 1 Porsche Panamera Sedan 2012, 7.4ms
image 1329/1524 /content/data/images/test/test_07013.jpg: 256x320 1 Jaguar XK XKR 2012, 7.5ms
image 1330/1524 /content/data/images/test/test_07016.jpg: 256x320 1 Chevrolet Monte Carlo Coupe 2007, 7.8ms
image 1331/1524 /content/data/images/test/test_07017.jpg: 192x320 1 Cadillac CTS-V Sedan 2012, 9.1ms
image 1332/1524 /content/data/images/test/test_07021.jpg: 224x320 1 Jeep Grand Cherokee SUV 2012, 7.6ms
image 1333/1524 /content/data/images/test/test_07024.jpg: 224x320 1 Mercedes-Benz C-Class Sedan 2012, 7.3ms
image 1334/1524 /content/data/images/test/test_07036.jpg: 256x320 1 BMW 6 Series Convertible 2007, 7.5ms
image 1335/1524 /content/data/images/test/test_07040.jpg: 224x320 1 Cadillac Escalade EXT Crew Cab 2007, 7.5ms
image 1336/1524 /content/data/images/test/test_07047.jpg: 256x320 1 Chevrolet Corvette Convertible 2012, 1 Chevrolet Corvette ZR1 2012, 7.8ms
image 1337/1524 /content/data/images/test/test_07048.jpg: 128x320 1 Acura TSX Sedan 2012, 7.5ms
image 1338/1524 /content/data/images/test/test_07056.jpg: 192x320 1 Chrysler 300 SRT-8 2010, 12.9ms
image 1339/1524 /content/data/images/test/test_07058.jpg: 256x320 1 Chrysler PT Cruiser Convertible 2008, 8.2ms
image 1340/1524 /content/data/images/test/test_07060.jpg: 224x320 1 Jeep Wrangler SUV 2012, 8.0ms
image 1341/1524 /content/data/images/test/test_07062.jpg: 192x320 1 Audi S6 Sedan 2011, 7.3ms
image 1342/1524 /content/data/images/test/test_07066.jpg: 224x320 1 Acura ZDX Hatchback 2012, 7.5ms
image 1343/1524 /content/data/images/test/test_07067.jpg: 256x320 1 Hyundai Elantra Touring Hatchback 2012, 7.8ms
image 1344/1524 /content/data/images/test/test_07071.jpg: 256x320 1 Chevrolet Sonic Sedan 2012, 7.2ms
image 1345/1524 /content/data/images/test/test_07075.jpg: 224x320 1 Volvo XC90 SUV 2007, 10.6ms
image 1346/1524 /content/data/images/test/test_07078.jpg: 256x320 1 GMC Yukon Hybrid SUV 2012, 7.5ms
image 1347/1524 /content/data/images/test/test_07087.jpg: 224x320 1 Bentley Continental GT Coupe 2012, 7.6ms
image 1348/1524 /content/data/images/test/test_07093.jpg: 256x320 1 Jeep Grand Cherokee SUV 2012, 7.6ms
image 1349/1524 /content/data/images/test/test_07094.jpg: 224x320 1 Audi S5 Coupe 2012, 7.5ms
image 1350/1524 /content/data/images/test/test_07104.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 7.2ms
image 1351/1524 /content/data/images/test/test_07107.jpg: 224x320 1 Bugatti Veyron 16.4 Coupe 2009, 7.5ms
image 1352/1524 /content/data/images/test/test_07118.jpg: 224x320 1 Hyundai Tucson SUV 2012, 9.3ms
image 1353/1524 /content/data/images/test/test_07129.jpg: 160x320 1 Lamborghini Diablo Coupe 2001, 8.9ms
image 1354/1524 /content/data/images/test/test_07132.jpg: 224x320 1 Cadillac SRX SUV 2012, 9.2ms
image 1355/1524 /content/data/images/test/test_07136.jpg: 256x320 1 Bentley Mulsanne Sedan 2011, 7.6ms
image 1356/1524 /content/data/images/test/test_07138.jpg: 224x320 1 Buick Regal GS 2012, 7.5ms
image 1357/1524 /content/data/images/test/test_07140.jpg: 256x320 1 Ford F-450 Super Duty Crew Cab 2012, 8.1ms
image 1358/1524 /content/data/images/test/test_07144.jpg: 224x320 1 Chevrolet Corvette ZR1 2012, 7.5ms
image 1359/1524 /content/data/images/test/test_07148.jpg: 224x320 1 Ford E-Series Wagon Van 2012, 7.3ms
image 1360/1524 /content/data/images/test/test_07150.jpg: 224x320 1 Audi TT RS Coupe 2012, 7.3ms
image 1361/1524 /content/data/images/test/test_07153.jpg: 224x320 1 BMW X3 SUV 2012, 7.4ms
image 1362/1524 /content/data/images/test/test_07165.jpg: 288x320 1 Mercedes-Benz SL-Class Coupe 2009, 8.0ms
image 1363/1524 /content/data/images/test/test_07168.jpg: 256x320 1 Audi S4 Sedan 2012, 7.7ms
image 1364/1524 /content/data/images/test/test_07170.jpg: 224x320 1 Chevrolet Tahoe Hybrid SUV 2012, 7.6ms
image 1365/1524 /content/data/images/test/test_07182.jpg: 224x320 1 Dodge Dakota Club Cab 2007, 9.1ms
image 1366/1524 /content/data/images/test/test_07183.jpg: 288x320 1 Dodge Dakota Club Cab 2007, 8.2ms
image 1367/1524 /content/data/images/test/test_07184.jpg: 224x320 1 Land Rover LR2 SUV 2012, 7.8ms
image 1368/1524 /content/data/images/test/test_07193.jpg: 256x320 1 Dodge Dakota Club Cab 2007, 7.6ms
image 1369/1524 /content/data/images/test/test_07210.jpg: 224x320 1 Hyundai Azera Sedan 2012, 7.6ms
image 1370/1524 /content/data/images/test/test_07214.jpg: 256x320 1 Dodge Charger Sedan 2012, 7.7ms
image 1371/1524 /content/data/images/test/test_07215.jpg: 224x320 1 Chevrolet Avalanche Crew Cab 2012, 7.5ms
image 1372/1524 /content/data/images/test/test_07216.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 7.6ms
image 1373/1524 /content/data/images/test/test_07224.jpg: 256x320 1 Audi V8 Sedan 1994, 7.6ms
image 1374/1524 /content/data/images/test/test_07226.jpg: 224x320 1 Audi TT RS Coupe 2012, 7.9ms
image 1375/1524 /content/data/images/test/test_07230.jpg: 320x224 1 Dodge Challenger SRT8 2011, 7.7ms
image 1376/1524 /content/data/images/test/test_07236.jpg: 256x320 1 smart fortwo Convertible 2012, 7.5ms
image 1377/1524 /content/data/images/test/test_07247.jpg: 192x320 1 Dodge Dakota Club Cab 2007, 7.4ms
image 1378/1524 /content/data/images/test/test_07248.jpg: 256x320 1 Hyundai Genesis Sedan 2012, 7.6ms
image 1379/1524 /content/data/images/test/test_07255.jpg: 128x320 1 Acura TL Sedan 2012, 8.2ms
image 1380/1524 /content/data/images/test/test_07256.jpg: 256x320 1 Dodge Durango SUV 2012, 7.7ms
image 1381/1524 /content/data/images/test/test_07257.jpg: 256x320 1 Chevrolet HHR SS 2010, 7.3ms
image 1382/1524 /content/data/images/test/test_07262.jpg: 320x320 1 Hyundai Azera Sedan 2012, 9.1ms
image 1383/1524 /content/data/images/test/test_07267.jpg: 224x320 1 Honda Odyssey Minivan 2012, 8.3ms
image 1384/1524 /content/data/images/test/test_07271.jpg: 256x320 1 Bentley Continental GT Coupe 2007, 8.6ms
image 1385/1524 /content/data/images/test/test_07277.jpg: 224x320 1 Volkswagen Golf Hatchback 2012, 7.4ms
image 1386/1524 /content/data/images/test/test_07280.jpg: 256x320 1 GMC Acadia SUV 2012, 7.6ms
image 1387/1524 /content/data/images/test/test_07284.jpg: 160x320 1 Audi 100 Sedan 1994, 7.1ms
image 1388/1524 /content/data/images/test/test_07294.jpg: 224x320 1 Jeep Wrangler SUV 2012, 7.6ms
image 1389/1524 /content/data/images/test/test_07312.jpg: 256x320 1 Suzuki Aerio Sedan 2007, 7.5ms
image 1390/1524 /content/data/images/test/test_07313.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 7.7ms
image 1391/1524 /content/data/images/test/test_07325.jpg: 224x320 1 Chevrolet Express Cargo Van 2007, 1 Chevrolet Express Van 2007, 7.5ms
image 1392/1524 /content/data/images/test/test_07326.jpg: 224x320 1 Chevrolet Tahoe Hybrid SUV 2012, 7.3ms
image 1393/1524 /content/data/images/test/test_07332.jpg: 256x320 1 Ford Freestar Minivan 2007, 7.5ms
image 1394/1524 /content/data/images/test/test_07337.jpg: 256x320 1 Geo Metro Convertible 1993, 7.5ms
image 1395/1524 /content/data/images/test/test_07339.jpg: 256x320 1 BMW 6 Series Convertible 2007, 7.8ms
image 1396/1524 /content/data/images/test/test_07345.jpg: 256x320 1 Land Rover Range Rover SUV 2012, 7.8ms
image 1397/1524 /content/data/images/test/test_07349.jpg: 224x320 1 Honda Odyssey Minivan 2007, 7.8ms
image 1398/1524 /content/data/images/test/test_07363.jpg: 256x320 1 Nissan Juke Hatchback 2012, 8.9ms
image 1399/1524 /content/data/images/test/test_07365.jpg: 224x320 1 Dodge Caliber Wagon 2007, 11.6ms
image 1400/1524 /content/data/images/test/test_07372.jpg: 256x320 1 BMW M6 Convertible 2010, 7.9ms
image 1401/1524 /content/data/images/test/test_07380.jpg: 224x320 1 Lamborghini Diablo Coupe 2001, 1 McLaren MP4-12C Coupe 2012, 9.2ms
image 1402/1524 /content/data/images/test/test_07381.jpg: 256x320 1 Audi 100 Sedan 1994, 7.5ms
image 1403/1524 /content/data/images/test/test_07387.jpg: 256x320 1 Aston Martin Virage Convertible 2012, 7.9ms
image 1404/1524 /content/data/images/test/test_07390.jpg: 256x320 1 Chevrolet Traverse SUV 2012, 7.3ms
image 1405/1524 /content/data/images/test/test_07397.jpg: 192x320 1 Geo Metro Convertible 1993, 7.1ms
image 1406/1524 /content/data/images/test/test_07399.jpg: 192x320 1 Ferrari California Convertible 2012, 6.9ms
image 1407/1524 /content/data/images/test/test_07403.jpg: 224x320 1 Bentley Continental GT Coupe 2012, 7.5ms
image 1408/1524 /content/data/images/test/test_07410.jpg: 256x320 1 Ford Expedition EL SUV 2009, 7.5ms
image 1409/1524 /content/data/images/test/test_07416.jpg: 224x320 1 Dodge Challenger SRT8 2011, 7.4ms
image 1410/1524 /content/data/images/test/test_07417.jpg: 256x320 1 Chevrolet HHR SS 2010, 7.7ms
image 1411/1524 /content/data/images/test/test_07422.jpg: 224x320 1 Cadillac Escalade EXT Crew Cab 2007, 1 Chevrolet Avalanche Crew Cab 2012, 7.8ms
image 1412/1524 /content/data/images/test/test_07426.jpg: 256x320 1 Ferrari 458 Italia Coupe 2012, 7.6ms
image 1413/1524 /content/data/images/test/test_07429.jpg: 192x320 1 Jeep Compass SUV 2012, 7.6ms
image 1414/1524 /content/data/images/test/test_07448.jpg: 192x320 1 Ford Fiesta Sedan 2012, 7.1ms
image 1415/1524 /content/data/images/test/test_07450.jpg: 256x320 1 Chevrolet Corvette ZR1 2012, 7.8ms
image 1416/1524 /content/data/images/test/test_07459.jpg: 160x320 1 Land Rover LR2 SUV 2012, 7.4ms
image 1417/1524 /content/data/images/test/test_07460.jpg: 256x320 1 Ford Ranger SuperCab 2011, 7.8ms
image 1418/1524 /content/data/images/test/test_07463.jpg: 160x320 1 Lincoln Town Car Sedan 2011, 7.3ms
image 1419/1524 /content/data/images/test/test_07464.jpg: 224x320 1 Lincoln Town Car Sedan 2011, 7.6ms
image 1420/1524 /content/data/images/test/test_07483.jpg: 224x320 1 Chevrolet HHR SS 2010, 7.6ms
image 1421/1524 /content/data/images/test/test_07494.jpg: 256x320 1 Ford Freestar Minivan 2007, 1 Suzuki SX4 Hatchback 2012, 7.9ms
image 1422/1524 /content/data/images/test/test_07498.jpg: 256x320 1 Volvo 240 Sedan 1993, 7.3ms
image 1423/1524 /content/data/images/test/test_07500.jpg: 224x320 1 Chevrolet Cobalt SS 2010, 9.4ms
image 1424/1524 /content/data/images/test/test_07508.jpg: 256x320 1 Cadillac SRX SUV 2012, 7.7ms
image 1425/1524 /content/data/images/test/test_07518.jpg: 256x320 1 Chevrolet Corvette ZR1 2012, 7.7ms
image 1426/1524 /content/data/images/test/test_07527.jpg: 256x320 1 Dodge Charger Sedan 2012, 7.9ms
image 1427/1524 /content/data/images/test/test_07545.jpg: 256x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 13.0ms
image 1428/1524 /content/data/images/test/test_07551.jpg: 224x320 1 Buick Verano Sedan 2012, 8.4ms
image 1429/1524 /content/data/images/test/test_07553.jpg: 256x320 1 Chrysler Crossfire Convertible 2008, 8.4ms
image 1430/1524 /content/data/images/test/test_07557.jpg: 224x320 1 Mitsubishi Lancer Sedan 2012, 9.3ms
image 1431/1524 /content/data/images/test/test_07564.jpg: 192x320 1 Suzuki SX4 Sedan 2012, 8.2ms
image 1432/1524 /content/data/images/test/test_07573.jpg: 256x320 1 Cadillac CTS-V Sedan 2012, 8.8ms
image 1433/1524 /content/data/images/test/test_07586.jpg: 224x320 1 Chrysler Town and Country Minivan 2012, 8.1ms
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image 1445/1524 /content/data/images/test/test_07645.jpg: 256x320 1 BMW 1 Series Coupe 2012, 10.2ms
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image 1451/1524 /content/data/images/test/test_07675.jpg: 256x320 1 Dodge Caravan Minivan 1997, 8.1ms
image 1452/1524 /content/data/images/test/test_07678.jpg: 256x320 1 Daewoo Nubira Wagon 2002, 8.3ms
image 1453/1524 /content/data/images/test/test_07685.jpg: 256x320 1 Chevrolet Avalanche Crew Cab 2012, 7.4ms
image 1454/1524 /content/data/images/test/test_07702.jpg: 256x320 1 Chevrolet Silverado 1500 Regular Cab 2012, 7.4ms
image 1455/1524 /content/data/images/test/test_07708.jpg: 224x320 1 Buick Regal GS 2012, 7.9ms
image 1456/1524 /content/data/images/test/test_07711.jpg: 192x320 1 GMC Yukon Hybrid SUV 2012, 9.3ms
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image 1460/1524 /content/data/images/test/test_07738.jpg: 256x320 1 Bugatti Veyron 16.4 Coupe 2009, 7.3ms
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image 1520/1524 /content/data/images/test/test_08032.jpg: 224x320 1 Chevrolet Silverado 2500HD Regular Cab 2012, 1 Chevrolet Silverado 1500 Regular Cab 2012, 8.1ms
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image 1523/1524 /content/data/images/test/test_08036.jpg: 256x320 1 Suzuki SX4 Sedan 2012, 10.5ms
image 1524/1524 /content/data/images/test/test_08041.jpg: 224x320 1 BMW X5 SUV 2007, 8.1ms
Speed: 0.2ms pre-process, 9.3ms inference, 1.0ms NMS per image at shape (1, 3, 320, 320)
Results saved to yolov5/runs/detect/exp

for i in ['/content/yolov5/runs/detect/exp/test_00047.jpg', '/content/yolov5/runs/detect/exp/test_00018.jpg']:
  img = Image(filename=i, width=500)
  display(img)

Final inferences

We achieved a recall of 93% and mAP50 of 96% on the test images with YOLO V5 S model.

Speed: 0.1ms pre-process, 1.6ms inference, 2.1ms NMS per image.

The model is very light weight as well with 15.3 MB. (size of 'best.pt' weights)

Basic Clickable UI using Gradio

!pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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import torch

best_model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5_best.pt') # Load the saved best model

Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master
YOLOv5 🚀 2022-11-20 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)

Fusing layers... 
Model summary: 157 layers, 7538737 parameters, 0 gradients, 17.4 GFLOPs
Adding AutoShape... 

def predict(input_image): # function to take image input and output an image with predictions

  pred = best_model(input_image, size=320)
  pred_image = pred.render()[0]

  return pred_image

import gradio as gr

# Launch the basic UI for prediction
gr.Interface(fn=predict, 
             inputs=gr.Image(),
             outputs=gr.Image(shape=(224, 224))).launch() # Note that this would launch an interface. This seems not visible after saving the notebook. Attached the screenshot in report.

Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`
Note: opening Chrome Inspector may crash demo inside Colab notebooks.

To create a public link, set `share=True` in `launch()`.